What each curve unlocks. HyperSenseIQ reads standard wireline and LWD curves and automatically maps common mnemonic aliases. Each output depends on specific curves being present: GR (gamma ray) drives shale volume, the clean/shale baseline and the pay cutoffs; RHOB (bulk density) and/or NPHI (neutron) give porosity; a deep resistivity curve (RT, or aliases such as ILD, LLD, RMED, M2RX) gives water saturation via Archie, and therefore net pay; DT (sonic) unlocks geomechanics — UCS and elastic properties. Optional curves add capability: caliper enables washout QC, a temperature curve auto-populates PVT, and operator-interpreted PHIE / SW / PERM / VSH curves are used directly when present.

The standardisation reality. LAS is a loose standard — curve names, units and completeness vary by tool, vintage and operator. The platform maps a living list of mnemonic aliases so it recognises most naming conventions, but it cannot invent data that is not in the file. Two files from the same field can carry entirely different curve sets.

What happens when a curve is missing — the important part. HyperSenseIQ does not substitute a placeholder value into pay. It computes results only where the required curve is genuinely present, and it reports the coverage explicitly. On upload you will see a data coverage caution stating the resistivity (Sw) and porosity coverage percentages and listing exactly what could not be determined. For example, a file whose resistivity curve is present for only 10% of the interval will report Sw and pay for that 10% and flag the remaining 90% as undetermined — rather than fabricating a large net-pay figure on a default saturation. A sample cannot be classified as pay unless it has a genuinely computed porosity and a genuinely computed water saturation.

Minimum for a complete evaluation. A LAS should contain at least GR, a porosity curve (RHOB or NPHI), and a deep resistivity curve (RT). Add DT (sonic) to also obtain geomechanics. A file missing resistivity can still be viewed for lithology and porosity, but water saturation and pay cannot be computed and will be flagged as such. If your organisation can supply LAS files with a consistent curve set — or with operator-interpreted PHIE/SW/PERM/VSH curves — the platform uses them directly and coverage is complete.

Net pay corrected — was inflated by merged shale gaps: When contiguous pay intervals are grouped into consolidated zones (small internal shale breaks merged for display, per formation-specific lamination-gap rules), the reported net pay previously used the zone's gross top-to-base span rather than the true summed pay footage inside it — silently inflating net pay by the width of every merged gap (e.g. 255.6m reported vs 234.4m true on Well X at a 1.5m gap threshold). Net pay now correctly reflects only genuine pay thickness; grouping still only changes how zones are displayed, not the total, matching the platform's own stated rule.

Pay Zones count added to the summary KPI row: Well Intelligence now shows a "Pay Zones" tile alongside Net Pay, N/G, Porosity, Sw and Permeability, reporting the count of contiguous pay intervals found.

Porosity-Permeability Crossplot (Timur): A new log-log crossplot on the Your Well Data tab shows permeability vs porosity for pay-zone samples, with a least-squares power-law trendline (k = k0·φⁿ) fitted per Timur (1968) — alongside the existing Density-Neutron and Pickett crossplots.

Bond Index Profile added to Well Integrity: CBL/SBT analysis now includes a depth-oriented Bond Index curve overlaid on colour-coded classification bands (Excellent/Good/Moderate/Partial/Poor), so cement quality trends are visible at a glance instead of only in the summary table. Reference: API RP 10B-2.

Real Arps decline fit from uploaded production history: Uploading a production CSV now drives an actual grid-search Arps fit (qi, Di, b) against the observed monthly rates on the Decline tab — a genuine least-squares fit to this well's own data, separate from the existing fixed-parameter illustrative scenario chart. Reports decline-type classification (Exponential/Hyperbolic/Harmonic), fit quality (R²), and EUR via the correct closed-form Arps integral to the platform's 25 bbl/d economic limit.

Water cut trend forecasting: A logistic S-curve is fitted to the uploaded water-cut history (full 3-parameter grid search, not a fixed heuristic — an earlier anchored-asymptote approach was found to bias the fitted parameters against a synthetic ground-truth test and was replaced), showing the fitted saturation asymptote, steepness and inflection point, a 1- and 2-year forecast, and a waterflood-stage classification (Early / Transition / Water-dominant / Late-stage).

Production CSV parser rebuilt — was silently misreading columns: The CSV import previously guessed column meaning from count and numeric-parseability, which could silently corrupt well-formed files — for example, reading a WaterCut column as oil rate, or a CumulativeOil column as the rate column, with no warning. It now matches columns by header name (exact match first, then pattern match with explicit exclusions, e.g. excluding "cum" from the rate match) and fails with a clear message naming the missing column when a required field cannot be found, rather than producing silently wrong numbers. It also correctly handles a blank cell (e.g. a missing month's water-cut reading) without shifting every subsequent column.

Holmes-Buckles Irreducible Water Saturation — corrected to the Buckles relationship: The default equation for irreducible water saturation (Swirr) is now the Buckles form, Swirr = C / φ, with C = 0.04 — the standard constant for clean sandstones, where the product Swirr × φ is approximately constant (Buckles 1965). The previous default was a power-law, Swirr = 0.03 × φ^(−3.5), which was calibrated for tight, low-porosity sandstones and is physically inappropriate for high-porosity clastics: at 28–40% porosity it saturated Swirr at its cap and returned an irreducible water saturation higher than the measured total water saturation in the majority of pay samples — an impossible result that flagged genuinely producible zones as non-commercial. The Buckles form scales correctly with porosity (Swirr ≈ 10–14% at these porosities) and produces physically consistent movable-water estimates.

Selectable calibration mode: A calibration-mode dropdown lets the user choose Buckles (C/φ) for high-porosity clastics or the legacy power-law (λ₀ × φ^λ₁) for tight or low-porosity formations. The Buckles constant C is user-editable (default 0.04) so it can be tuned to core data where available. Swirr here is model-derived from porosity, not a laboratory measurement — the panel notes that core calibration is recommended for precision.

Zone status and thickness corrections: Zone producibility status is now derived from the zone's average movable water rather than inherited from its first sample, so a zone that is producible on average is no longer mislabelled non-commercial because of a single high-water sample at its top. Zone thickness is now computed as (base − top) + one sampling step, which correctly reports single-sample zones as one step of formation (~0.15 m) instead of 0.0 m. Reference: Buckles RS (1965).

Universal data-deficiency handling (no fabricated pay): A sample is now classified as pay only when it has both a genuinely computed porosity and a genuinely computed water saturation. Previously, a well with porosity but no resistivity had its water saturation defaulted to 0.50, which silently passed the pay cutoff and fabricated large net-pay figures on files whose resistivity curve was absent or largely null. Every upload now reports a data-coverage caution stating the resistivity and porosity coverage percentages and exactly what could not be determined. See the dedicated FAQ entry on LAS coverage for details.

Real Geomechanics on Drilling Physics tab (for real LAS uploads with a sonic curve): Dynamic Young's Modulus and Poisson's Ratio are computed from actual sonic transit time (Vp, and Vs where a shear sonic curve is present; estimated via Castagna's 1985 mudrock line and clearly flagged when only compressional sonic exists). Overburden stress (Sv) is integrated from the real density log across the logged interval, with any unlogged shallow gap using a clearly labeled, editable assumed density rather than an invented trend. UCS, friction angle, and cohesion are computed using peer-reviewed, correctly cited correlations (Chang, Zoback & Khaksar 2006, J. Pet. Sci. Eng. 51:223-237 — UCS from McNally 1987, friction angle from Weingarten & Perkins 1995; cohesion derived via standard Mohr-Coulomb inversion, not an empirical guess). For any well without a sonic curve, the tab honestly states geomechanics is unavailable rather than showing a fabricated number.

Eaton (1975) Pore Pressure, Fracture Gradient, and Mud Weight Window: Pore pressure is estimated from the sonic log using the standard Eaton method (SPE 5544), with a normal compaction trend fitted by linear regression to shale-only points (Vsh > 0.6) in the well. The fit quality (R²) and number of shale points used are always shown alongside the result — if the trend is too weak (fewer than 30 shale points or R² below 0.3) the platform declines to report a number rather than show a false-confidence figure. Fracture gradient and a safe mud weight window follow from the pore pressure, overburden, and Poisson's ratio, with a standing note to calibrate against offset well data before operational use.

Flow-Unit Consolidation for real LAS uploads: Pay zones from an uploaded log are now grouped into geologically meaningful flow units — pay stretches separated by a thin non-pay gap (default 2.0m, editable) are merged into one zone, matching standard practice for treating thin shale laminations as internal heterogeneity rather than separate flow-unit boundaries. Total net pay is unaffected by this setting; only the zone grouping shown in the table changes. This replaced an earlier raw, un-merged interval count that could produce far more zones than were geologically meaningful.

STOIIP Parameters — explicit, editable inputs: Area and Bo are not measured by any LAS file and are now shown as clearly labeled, editable default values (10 acres, Bo=1.2) rather than silently assumed. STOIIP recalculates live as either is edited, and the same Area/Bo are now used consistently everywhere STOIIP is shown for a given well.

Reference Wells vs. real LAS uploads — data source honesty: Fields a wireline log cannot measure (production rate, decline rate, water cut, economics assumptions) are cleared rather than silently defaulted whenever a real LAS file is uploaded, requiring deliberate entry before running an analysis. Loading a Field Study reference well fully replaces all fields with that well's own consistent data set — real petrophysics and real production data are never blended from two different wells into one analysis.

Well Integrity, EOR Screening, and cross-tab consistency: Fixed several tabs that were silently falling back to Mumbai High demo defaults instead of reading the actual uploaded well. All entry points for loading a real or demo well (buttons in Your Well Data and the Downloads gallery) now route through one shared, verified pipeline.

Simandoux (1963) Equation — Third Sw option: Added alongside Archie and Indonesia in the Sw Equation dropdown in the Archie Parameters panel. Correct form: 1/Rt = (φ^m·Sw²)/(a·Rw) + Vsh·Sw/Rsh — solved as quadratic in Sw. When Vsh=0 this reduces exactly to Archie. For shaly formations it gives Sw between Archie and Indonesia. Reference: Asquith and Krygowski (2004) AAPG Methods in Exploration No.16, p.89. The Rsh input panel appears when either Indonesia or Simandoux is selected.

Sw Equation Comparison Panel: A new three-row table appears automatically below the crossplots after any LAS upload. It shows Archie, Indonesia, and Simandoux side by side with average Sw in pay zones, net pay in metres, and N/G percentage for each equation. The equation giving the most net pay is highlighted in green. This allows the petrophysicist to immediately see how sensitive the pay zone classification is to the choice of shaly sand correction. Rsh used and all Archie parameters are shown in the footnote.

Temperature-Corrected Rw from Formation Water Salinity: A new "Estimate Rw from Formation Water Salinity" panel appears below the Rw input in the Archie Parameters section. Enter NaCl salinity in ppm (mg/L) and formation temperature in °F — the temperature auto-populates from the LAS BHT curve when a file is loaded. Click Compute Rw to fill the Rw field automatically. Uses the Schlumberger Gen-9 chart relationship with Arps (1953) temperature correction. Important: this is a guide value with approximately ±20% accuracy — always verify with laboratory analysis of formation water samples or with the SP log before committing Rw to a reserves calculation. Reference: Schlumberger Log Interpretation Charts Gen-9; Arps JJ (1953) Trans AIME.

Pay Zone Interval Table — Enhanced columns: The contiguous pay zone interval table (visible in Pay Zones Only view) now shows seven columns: zone number, top depth, base depth, thickness, average porosity, average water saturation, and average permeability — for every individual pay interval separately. Previously only three columns were shown and the detection logic incorrectly collapsed all pay levels into one zone. Now correctly identifies each separate pay interval with its own reservoir properties.

Economics Tab — Fiscal auto-detect from LAS operator field: When a real LAS file is uploaded, the Economics tab now reads the COMP (company) header field and automatically selects the appropriate Indian fiscal terms. OIL India files use PEL (Petroleum Exploration Licence, 85% equity). Cairn or Vedanta files use RSC (Revenue Sharing Contract, 70% equity). All other files default to ONGC PSC (Production Sharing Contract, 85% equity). The net revenue label now shows the actual equity percentage being used. Anonymised files (COMP = "ABC") correctly default to PSC 85%.

Petrophysical Crossplots (9 July): Density-Neutron crossplot (NPHI vs RHOB) with sandstone, limestone, and dolomite lithology lines from Schlumberger Chart Por-1, and Pickett plot (log phi vs log Rt) with Sw contour lines at 100%, 75%, 50%, and 25%. Both appear automatically after uploading a LAS file. A Net Pay Sensitivity table (6×5 grid of Sw and Vsh cutoff combinations, current default highlighted) is also generated. Ref: Schlumberger (2009), Pickett (1973) SPWLA, Worthington (1994) SPE-28818.

Indonesia / Poupon-Leveaux Equation (9 July): The Archie Parameters panel now offers Archie (1942) for clean sands or Indonesia/Poupon-Leveaux (1971) for shaly sands. Recommended for Tipam, Kalol, Hazad, Fatehgarh — where clay conductivity causes Archie to overestimate water saturation. Rsh input (shale resistivity, typically 1–4 ohm-m) appears on selection. Solved as quadratic in √Sw.

Petrel-Style Downloadable Well Evaluation Report (9 July): Gold button "Download Petrel-Style Well Evaluation Report" appears after loading a LAS file. Contains 6-track SVG log display (GR, Vsh, Porosity, Sw, HC Saturation), pay zone inventory with depths and petrophysical averages, full equations section with published references, and letterhead. Open in browser and Print to PDF (A4 Landscape recommended).

Log Viewer Depth Direction and Pay Zones Only (8 July): Log viewer now displays shallow at top, deep at bottom — standard wireline convention. "Pay Zones Only" toggle shows only pay-flagged intervals at larger scale with a contiguous interval table (top, base, thickness). Real raw GR values now plotted in Track 1 instead of Vsh proxy. Implemented following DGH/MoPNG meeting feedback (8 July 2026).

EOR Screening Calibration (8 July): EOR candidacy scores recalibrated against Bera, Vij and Shah (2021), J Pet Sci Eng 207:109196, using Indian field performance data. ASP peor 0.85→0.78 (Mangala still pilot). CO2 Miscible uplift 0.38→0.45 (Gandhar field +50% EOR recovery). Thermal peor 0.65→0.62 (Balol/Santhal ISC data). GoI October 2018 EOGR policy now referenced in FAQ — platform directly serves mandatory EOR screening for all fields over 3 years operation.

Mud and Cement Engineering Module (7 July): New tab between Drilling Physics and Well Integrity. Five panels: Mud Weight Window and ECD (API RP 13D), Hole Cleaning/CTR (Sifferman 1974), Surge/Swab Safety (Burkhardt 1961), Mud Stability Indicators (Bern 2006 SPE-103402), Cementing Readiness Assessment (Nelson and Guillot 2006) — READY/CONDITIONAL/NOT READY verdict. Inputs auto-populate per formation on well switch.

Fluid PVT Module (4 July): New tab. Computes Rs (Standing 1947), Bo (Vasquez-Beggs 1980), bubble point, live oil viscosity (Beal 1946, Chew-Connally 1959), water FVF (Dodson-Standing 1944), and a pressure depletion table. Formation temperature auto-populates from DHAT curve in LAS. Reservoir pressure intentionally not auto-populated — unreliable in tectonically complex Indian basins.

GR Normalisation Override (4 July): Engineers can override the automatic P5/P95 GR baseline with geological picks. Results recompute live — Vsh, net pay, N/G all update instantly. Matches Petrel's GR normalisation workflow exactly.

Bourdet Derivative (3 July): PTA/RTA tab now produces automatic Bourdet derivative (dp/d ln dt) alongside Horner plot. Log-log chart, data table, flow regime classification (Wellbore Storage, Radial Flow, Transitional, Boundary Effect), and confidence rating. Validated: plateau = m/ln(10). Fires automatically when ptaRun() completes.

CBL/SBT Well Integrity Module (3 July): Well Integrity tab now accepts real CBL and SBT LAS files. Produces bond quality classification, azimuthal channelling analysis from SBT six-pad data, annular communication risk (MINIMAL/LOW/MODERATE/HIGH), and downloadable HTML integrity report with Print to PDF pathway.

Every calculation in HyperSenseIQ is a direct implementation of a named, published, peer-reviewed equation. No black box, no neural network, no proprietary formula. The engine runs entirely inside your browser as JavaScript — the source is the single HTML file at hypersenseiq.com. Right-click → View Page Source and search any function name (parseLAS, ptaRun, calcPVT, calcMud) to see the exact code. Below is the complete equation reference.

Every output traces to a peer-reviewed equation documented in the FAQ tab. The platform does not ask to be trusted on the strength of its branding — it asks to be evaluated on the transparency of its physics. The complete equation reference (all 15 equation groups with published references) is available in the "What equations does the platform use?" entry above. The code itself is visible — right-click on hypersenseiq.com, View Page Source, and search any function name to see the exact implementation. A field-executed validation study on seven mature Indian sandstone wells, where EOR advisory outputs matched actual field outcomes, provides the empirical evidence. Everything else can be verified in the browser right now.

HyperSenseIQ is an AI-powered subsurface intelligence platform designed for the Indian oil and gas sector. It takes wireline LAS files and well data as input and delivers instant reservoir characterisation, EOR screening, decline analysis, pressure transient analysis, drilling physics, economics and digital twin outputs — all in a unified browser interface. No installation required.

The core operational engine is entirely deterministic physics — not a neural network, not a black box. Every output is traceable to a named equation and a specific input value. STOIIP uses the standard volumetric equation. Decline uses Arps harmonic. Permeability uses the Craft, Hawkins and Terry Horner method. Every domain score in the EOR ranking is independently visible and challengeable. The AI layer (SWIR spectral engine) is patented and under development — clearly labelled as such.

The minimum requirement is a wireline LAS file with GR, RHOB, NPHI and RT curves, or a structured data sheet with porosity, permeability, pay thickness, water saturation, API gravity and production rate. The platform also accepts pressure buildup CSV files for PTA/RTA analysis. For the built-in demo, no data upload is needed — synthetic data for five Assam and five Mumbai High wells loads automatically.

Data entered in the browser stays in your browser session — it is not transmitted to any server. For PSU clients with strict data sovereignty requirements, a local deployment option is available where the entire platform runs within your own network. No data leaves OIL India or ONGC infrastructure under this arrangement.

Yes. The HyperSenseIQ SWIR spectral intelligence engine is covered by a patent application filed on 24 March 2026 with 11 claims. Six additional claims are drafted. The patent covers the transformer-based mineralogy identification and hydrocarbon contact prediction from hyperspectral core plug images.

The platform applies a five-criterion cutoff system to each depth interval: GR below the shale baseline identifies clean sand; RHOB and NPHI cross-over confirms gas or light oil; RT above the water baseline identifies hydrocarbon bearing zones; Sw calculated from Archie's equation determines fluid type. Zones meeting all criteria are flagged Pay. Zones meeting three or four are flagged Marginal.

For Cenozoic Indian sandstones (Kalol, Bassein, Tipam, Ankleshwar, Fatehgarh), the platform uses the Larionov (1969) Tertiary correction, a standard published method also available as a selectable model in Schlumberger Petrel. This corrects for the well-documented tendency of simple linear gamma ray index to overestimate shale content, particularly in clean-to-moderately-shaly sands. North Sea and chalk formations, being older, use the linear index since the Tertiary correction does not apply to their geological age.

Using the standard volumetric equation: STOIIP = 7758 × A × h × φ × (1 − Sw) / Bo, where A is drainage area in acres, h is net pay in feet, φ is effective porosity, Sw is water saturation and Bo is the formation volume factor. Every parameter is visible in the Your Well Data tab and can be adjusted.

It demonstrates the SWIR spectral intelligence engine on synthetic core plug data. Absorption peaks at 1730nm indicate oil signatures, 1900nm indicates brine, and 2200nm and above identify clay minerals, dolomite and mica. The engine cross-checks log-derived water saturation against spectral mineralogy to confirm or challenge the pay zone call. Real core plug image analysis is available for validation engagements.

It computes the full drilling envelope: pore pressure gradient using the Eaton method, fracture gradient using the Hubbert-Willis method, equivalent circulating density accounting for mud rheology and annular pressure losses, and the safe mud weight window. It flags kick risk when pore pressure approaches mud weight and lost circulation risk when ECD approaches the fracture gradient.

Yes, as a pre-drill screening and planning tool. The platform accepts pore pressure, fracture gradient and mud weight as inputs from your drilling records and delivers the safety envelope, ECD and MPD pressure limit. For detailed well design, the outputs inform rather than replace dedicated drilling software.

The platform evaluates seven EOR methods simultaneously: polymer flood, ASP, surfactant, CO₂ miscible, thermal, microbial, and UST. Each method is scored across six scientific domains — Physics, Mechanical, Geological, Chemical, Operational and Techno-Economic. The composite P_EOR score is the weighted average across all six domains. The highest scoring method is the primary recommendation, with the full ranking visible for every well.

The screening thresholds and candidacy uplift factors (upl) are calibrated against published screening criteria and Indian field performance data. Key references: Bera, Vij & Shah (2021), J Pet Sci Eng 207:109196 (Table 2 — global EOR screening parameters; Table 4 — Indian field performance: Sanand polymer +26%, Gandhar CO₂ miscible +50%, Balol/Santhal thermal +20-28%); a field-executed study under formal operator contract on mature Indian sandstone wells (UST validation).

Note: The Government of India's Enhanced Oil and Gas Recovery (EOGR) policy (October 2018) mandates EOR screening for all fields under operation for more than 3 years. HyperSenseIQ's EOR screening module directly serves this regulatory requirement. The seven key parameters for GoI-compliant EOR screening — API gravity, viscosity, reservoir depth, temperature, porosity, permeability, and oil saturation — are all derived automatically from a single LAS file upload.

Wettability describes whether the rock surface preferentially contacts oil or water. An oil-wet formation is harder to flood efficiently with water. HyperSenseIQ computes a wettability modifier coefficient from API gravity, connate water saturation and irreducible water saturation. This modifier adjusts the EOR score — chemical methods receive a higher uplift in oil-wet reservoirs, waterflood receives a penalty.

A thief zone is a high permeability streak that preferentially accepts injected fluid, bypassing lower permeability pay zones. HyperSenseIQ detects thief zones from the k/phi ratio diagnostic. When detected, it applies a TZ_pen penalty to injection-based EOR methods (waterflood, polymer, ASP) and flags the formation for conformance control evaluation before any injection programme. The diagnostic is now visible directly in the EOR Screening tab.

The Conformance Factor reflects the fitness of formation water chemistry for EOR injection methods. High total dissolved solids (TDS), hardness, scaling ions or incompatible brines reduce the CF_mod score for chemical EOR methods such as polymer and ASP. A CF_mod of 1.0 means the water is clean and unpenalised. Ankleshwar Hazad Member water, for example, carries a CF_mod of 0.68 due to high TDS and sulphate scaling tendency. UST and thermal methods are unaffected by CF_mod. The three modifiers — W_mod, CF_mod and TZ_pen — together adjust the raw EOR score through the master formula: P_EOR_adj = AVG × W_mod × CF_mod × (1 − TZ_pen).

These three modifiers are HyperSenseIQ's reservoir quality adjusters. W_mod (wettability modifier) reflects oil-wet versus water-wet tendencies from API gravity and water saturation data. TZ_pen (thief zone penalty) is triggered when the k/phi ratio exceeds 500 mD/fraction, indicating a high-permeability streak. CF_mod (conformance factor) reflects water chemistry fitness. Together they ensure the EOR ranking is not just based on generic physics but on the specific character of each well's reservoir. All three are now visible as diagnostic cards in the EOR Screening tab.

The Horner method analyses pressure buildup after shutting in a well. It plots shut-in pressure against log10 of the Horner time function (tp + Δt)/Δt. The straight line in the middle time region gives slope m in psi per log cycle, from which permeability k = 162.6qBμ/(|m|h) is calculated. The skin factor S tells you whether the wellbore is damaged (positive skin) or stimulated by fractures or acid (negative skin), and requires the flowing pressure before shut-in (Pwf) as a separate input — see the next question.

A skin of +5 indicates significant formation damage near the wellbore — likely from drilling mud invasion, scale deposition, clay swelling or fines migration. The well is producing at a fraction of its theoretical rate. A workover or acid stimulation to reduce skin to near zero can substantially restore productivity. The platform recommends intervention when skin exceeds 5. Skin factor requires the flowing pressure before shut-in (Pwf) entered in the PTA/RTA tab — this is real, physically required information for the standard skin equation, not a platform limitation. Without it, the platform shows skin as "—" rather than an unreliable number.

Yes. Click Upload CSV in the PTA/RTA tab and provide a file with two columns: time_hrs and pressure_psi. The platform parses it automatically, runs the Horner analysis and displays the plot and permeability within seconds. Enter the flowing pressure before shut-in (Pwf) in the field provided to also compute skin factor. The built-in synthetic data loads automatically for each well, including a consistent Pwf value, so you can see the capability without any data preparation.

Yes. The parser reconstructs each depth record from the underlying data stream regardless of how many physical lines it spans, so both WRAP=NO (one line per depth step) and WRAP=YES (multiple lines per depth step) LAS 2.0 files are read correctly.

The platform reads LAS 2.0 format, which covers the vast majority of Indian well archives. LAS 3.0 files can be exported in LAS 2.0 format from WellCAD, Techlog, or Petrel before upload. The standard petrophysical curves — GR, RHOB, NPHI, RT, DT — are common to both versions and will be correctly parsed once the file is in LAS 2.0 format.

The platform applies standard cutoffs uniformly to every depth level. Your log analyst may be applying formation-specific experience, local calibration, or geological judgement that the platform does not have. Enter your analyst's specific cutoff values in the Archie Parameters and GR Normalisation panels in the Your Well Data tab and see which zones survive. Discrepancies almost always resolve to a marginal zone sitting on the boundary of the cutoff — this is exactly why the cutoffs are fully adjustable. The Net Pay Sensitivity table (generated automatically after LAS upload) shows how net pay changes across a 6×5 grid of Sw and Vsh cutoff combinations, making the sensitivity immediately visible.

HyperSenseIQ STOIIP is a well-level estimate derived from one wireline log using: STOIIP = 7758 × A × h × phi × (1−Sw) / Bo. Your volumetric estimate uses a full-field reservoir model with mapped areal extent, structural closure, and isochore maps across multiple wells. The two numbers are answering different questions. The platform's STOIIP is a well-level consistency check — it tells you whether the single well's petrophysical parameters are internally consistent. Field-level STOIIP requires the full geological model. If both use the same phi, Sw, h, and Bo for a single well and still disagree, the difference is in the drainage area (A) or the Bo assumption — both can be adjusted in the Well Parameters section.

The log viewer displays Sw (water saturation computed from RT via Archie's equation) rather than the raw resistivity curve, because Sw is more directly interpretable for pay zone identification. The platform reads deep resistivity from A40H, P40H, RDEP, ILD, or LLD curve mnemonics depending on the tool. If the LAS file does not contain a recognised resistivity curve, the platform flags this in the parse result and Sw cannot be computed. The Pickett plot (generated automatically after LAS upload) plots log porosity vs log Rt and shows the raw resistivity relationship directly — pay zone points cluster to the right of the Sw=1 water line.

Archie (1942) assumes the formation is a clean sand — its resistivity is entirely controlled by the pore fluid. In shaly sands, clay minerals contribute their own electrical conductivity, which makes the formation appear more conductive than the hydrocarbon saturation alone would justify. Archie therefore overestimates water saturation in shaly formations.

The Indonesia / Poupon-Leveaux equation (1971) corrects for this by adding a shale conductivity term that depends on the shale volume (Vsh) and the shale resistivity (Rsh). For Indian Cenozoic formations — Tipam Sandstone in Assam, Kalol and Hazad in Cambay, Fatehgarh in Rajasthan — which are typically shaly sands rather than clean sands, the Indonesia equation gives a more accurate and more optimistic water saturation, reclassifying some borderline intervals from non-pay to pay.

The Rsh input (shale resistivity) can be read from the baseline resistivity in the shale intervals of your log — typically 1 to 4 ohm-m for Indian Cenozoic shales. To switch equations, open the Archie Parameters panel in the Your Well Data tab, select your preferred equation from the dropdown, enter Rsh, and click Recompute. A third option — Simandoux (1963) — is also available for dispersed clay formations; it gives Sw values between Archie and Indonesia. The Sw Equation Comparison panel below the crossplots shows all three equations side by side automatically after any LAS upload. Reference: Poupon A and Leveaux J (1971), Evaluation of Water Saturations in Shaly Formations, SPWLA 12th Annual Symposium. Simandoux P (1963), Revue de l'Institut Français du Pétrole.

The platform automatically normalises the gamma ray curve using the P5 and P95 of the GR distribution — the 5th percentile is used as the clean sand baseline and the 95th percentile as the shale line. This statistical method excludes outlier readings from thin coal seams or radioactive minerals that would otherwise inflate the shale baseline.

If your petrophysicist has defined specific GR clean and shale values from core data or regional geological knowledge, you can override the automatic values. Open the GR Normalisation panel in the Your Well Data tab (below the Archie Parameters panel), enter your GR clean sand value and GR shale line value in API units, and click Recompute Vsh, Net Pay and N/G. The log viewer and all petrophysical results will update instantly to reflect your geological picks. This matches Petrel's GR normalisation workflow exactly.

The Density-Neutron crossplot (NPHI vs RHOB) is the standard lithology identification tool in formation evaluation. Data points falling on the sandstone line indicate quartz-dominated matrix; on the limestone line, carbonate matrix; above the sandstone line, gas effect. Pay zone levels are shown in green, non-pay in grey. Reference: Schlumberger Log Interpretation Charts (2009), Chart Por-1.

The Pickett plot (log porosity vs log deep resistivity) is the graphical form of the Archie equation. The water saturation contour lines are drawn using your current Archie parameters. Data points plotting to the right of the Sw=1 water line indicate hydrocarbon presence. Pay zone points cluster to the right and above non-pay points. The slope of all contour lines equals -m (the cementation exponent). A petrophysicist uses this plot to verify Archie parameters and to visually confirm the pay zone classification before committing to a net pay estimate. Reference: Pickett GR (1973), SPWLA 14th Annual Symposium.

The Net Pay Sensitivity table shows how the net pay estimate changes when the Sw and Vsh cutoff values are varied. It is a 6x5 grid covering Sw cutoffs from 50% to 75% and Vsh cutoffs from 30% to 50%, with the current platform defaults (Sw 65%, Vsh 40%) highlighted in amber. This is a standard deliverable before presenting net pay figures to a reserves committee — it demonstrates that the result is not overly sensitive to small changes in cutoff choice and gives decision-makers a transparent view of the uncertainty. Reference: Worthington PF (1994), Effective Integration of Core and Log Data, SPE-28818.

Two report formats are available after uploading a LAS file in the Your Well Data tab. The Petrel-style HTML report (gold button — Download Petrel-Style Well Evaluation Report) produces a multi-track log display with GR, Vsh, Porosity, Sw, and HC Saturation tracks plotted against depth, a pay zone inventory table listing every interval with its top, base, thickness, average porosity, Sw, permeability, and HC saturation, and a full equations section citing each published reference. Open the downloaded HTML file in Chrome or Edge and use Print / Save as PDF to produce a PDF. Use A4 landscape for best fit.

The PDF Assessment Report (red button — Download Assessment Report) is generated directly in PDF format using the jsPDF library and contains the well identification, reservoir parameters, pay zone breakdown, Archie parameters used, EOR screening verdict, and production parameters. Both reports are generated entirely in your browser — no data leaves your machine at any point.

The Mud and Cement Engineering tab provides a pre-cement drilling fluids assessment covering five output panels. The Mud Weight Window and ECD panel shows the safe mud weight band between pore pressure and fracture gradient, and the effective circulating density (ECD) while pumping — the critical check that ECD does not exceed the fracture gradient. The Hole Cleaning panel computes the Cuttings Transport Ratio (CTR) using the Sifferman (1974) correlation and flags marginal hole cleaning. The Surge and Swab panel uses the Burkhardt (1961) method to estimate surge pressure on casing run-in and swab pressure on pipe pull-out. The Mud Stability Indicators panel flags sag risk for OBM and SBM systems and shale reactivity risk for WBM. The Cementing Readiness Assessment uses a five-factor weighted model to give a READY, CONDITIONAL, or NOT READY verdict before any cement job.

The module connects directly to the Well Integrity tab — it answers the question of whether pre-cement conditions were appropriate, complementing the CBL/SBT azimuthal analysis which answers whether the resulting cement bond is adequate. References: API RP 13D (2006), Sifferman et al. (1974) JPT, Burkhardt (1961) JPT, Bern et al. (2006) SPE-103402, Nelson and Guillot (2006) Well Cementing 2nd Ed.

The Fluid PVT tab computes the pressure-volume-temperature properties of reservoir fluids using standard published correlations. Six KPI outputs are shown: solution gas-oil ratio (Rs) using Standing (1947), oil formation volume factor (Bo) using Vasquez-Beggs (1980), bubble point pressure (Pb) using Standing (1947), live oil viscosity using Beal (1946) for dead oil and Chew-Connally (1959) for live oil, water formation volume factor using Dodson-Standing (1944), and water viscosity using Van Wingen (1950).

The tab also produces a pressure depletion table showing how Rs, Bo, and viscosity change as reservoir pressure declines from initial pressure to abandonment — critical for material balance accuracy and for deciding at what pressure an artificial lift conversion becomes necessary. The formation temperature is automatically read from the DHAT downhole temperature curve in the LAS file where available. The reservoir pressure is intentionally not auto-populated because hydrostatic estimates are unreliable in tectonically complex Indian basins — the engineer must enter the actual DST reservoir pressure.

It fits an Arps harmonic decline curve to the well's production history and extrapolates forward. The outputs are Estimated Ultimate Recovery (EUR), remaining reserves at abandonment rate, the current rate at any future point in time, and the Production History Factor (PHF). PHF is the ratio of predicted-to-actual cumulative production — a PHF above 1.1 means the well is declining faster than the model predicted and warrants investigation.

PHF stands for Production History Factor. It compares the Arps model prediction against what the well actually produced. A value of 1.0 means the model and reality agree. Above 1.1 flags accelerated decline — often a sign of water breakthrough, skin damage building up, or reservoir pressure depletion faster than expected. The platform highlights the well in amber when PHF exceeds 1.1 and flags it for PTA/RTA and EOR review.

The Economics tab takes the decline profile and overlays a fiscal model. It uses live Brent crude pricing converted to INR, applies GoI royalty, OID cess, and GST on services, and calculates field NPV, IRR, and payback period at the equity percentage you specify. All parameters — crude price, discount rate, opex, equity — are editable so you can run sensitivities. The undiscounted cash flow curve is shown alongside the NPV.

Live Brent crude is fetched from a public market data feed and displayed in the platform header. It refreshes every 15 minutes. If the live feed is unavailable, the last known price is used and a staleness indicator appears. You can override the price manually in the Economics tab for planning scenarios.

It estimates how much of the reservoir energy driving production comes from solution gas expansion versus water influx from an aquifer. The two outputs are P_ei — the solution gas drive index — and P_eaq — the aquifer drive index. Together they identify the dominant drive mechanism, which directly informs whether waterflood or gas injection is the appropriate pressure maintenance strategy.

P_ei is the Expansion Index for solution gas drive — OOIP divided by total reservoir energy, expressed as a percentage. P_eaq is the aquifer expansion index — water influx contribution divided by total energy. A P_ei above 60% indicates the well is predominantly solution gas driven with limited natural water support. A P_eaq above 40% indicates strong aquifer influx. The two should add to 100% along with any gas cap contribution tracked as P_ecap.

Yes. Upload a CSV with four columns — Well Name, OOIP (MMbbl), Aquifer Model, Water Influx (bbl). The platform skips the header row automatically, calculates drive indices for each well, and displays a comparison table alongside a drive mechanism bar chart. A template with all 7 reference wells pre-filled is available to download directly from the tab.

Three options: No Aquifer, Weak, and Strong. No Aquifer means all drive energy comes from solution gas and rock-fluid compressibility. Weak aquifer provides partial support but is insufficient for pressure maintenance without injection. Strong aquifer means natural water influx is dominant, which is favourable for recovery but can cause early water breakthrough in high-permeability streaks.

It is the primary data entry point for the platform. You enter well parameters here — porosity, permeability, pay thickness, water saturation, API gravity, depth, production rate, water cut and decline rate — and every analysis tab updates instantly with your values. The reference wells from the 9-month field study can be loaded with one click as a starting point, or you can enter your own well's actual parameters directly.

Six parameters give a meaningful first pass: porosity, water saturation, net pay thickness, permeability, initial production rate, and API gravity. With those six, the platform computes STOIIP, the EOR ranking, the decline curve and an economics estimate. Depth and water cut improve the drilling physics and production engineering outputs. Uploading a LAS file replaces the need to enter porosity, water saturation, permeability and pay thickness manually.

Yes, in real time. The platform recomputes all dependent analyses as you type. Change porosity and STOIIP updates. Change water cut and the EOR wettability modifier adjusts. Change the initial rate and the decline curve and EUR recalculate. You do not need to re-run anything manually — every tab reflects your current inputs continuously.

It creates a structured digital record of the well — national asset identity, live petrophysical attributes, and an append-only provenance log of every analysis run during the session. Each entry records the analysis type, the key output, and a timestamp. The intent is that each well analysed through HyperSenseIQ has a traceable, auditable record of its subsurface assessments independent of who ran them.

It is an append-only log stored in the browser session — not a distributed blockchain network. The design principle is immutability: once an analysis result is recorded, it is not overwritten. For PSU clients, this log can be exported and archived to create a verifiable audit trail of reservoir assessments — useful for internal governance, DGH reporting, and dispute resolution if production forecasts are later questioned.

It maps the well's digital twin attributes to the DGH national data repository schema — the format used by India's Directorate General of Hydrocarbons for well data submissions under NELP and DSF reporting requirements. Currently it displays the well's attributes formatted in that schema so the operator can verify alignment before submission. Direct API integration with the national repository is on the platform roadmap.

Four sub-modules: casing design (three-string program with burst, collapse and tension safety factors to API 5CT), production string sizing (tubing selection with Turner critical velocity check), bean valve and choke sizing (Gilbert 1954 correlation), and sucker rod pump sizing for artificial lift (API RP 11L). The reference case is anchored to Ankleshwar Hazad Member conditions, including H₂S service requirements and sand production risk.

It plots the Inflow Performance Relationship (IPR) from Vogel's (1968) equation against the Vertical Lift Performance (VLP) from the Hagedorn-Brown correlation. The intersection is the natural operating point — the rate and flowing bottomhole pressure at which the well stabilises. Changing reservoir pressure, wellhead pressure, GOR or water cut shifts the curves and the operating point updates in real time. This confirms whether the current completion can deliver the target rate or whether artificial lift is needed.

Both grades are required for sour service where H₂S is present. Standard J-55 casing can suffer hydrogen-induced cracking in sour environments. N-80 and L-80 are manufactured to NACE MR0175 specification, which means they are tested for resistance to sulphide stress cracking. The platform detects H₂S presence from the dataset and selects the appropriate grade automatically in the casing design output.

ALCF is the Artificial Lift Complexity Factor — a composite index computed from water cut, GOR, sand content, H₂S level and fluid viscosity. A high ALCF means the produced fluid is corrosive, abrasive or gassy, all of which accelerate pump wear and shorten workover intervals. The platform uses ALCF to set inspection frequency, recommend rod string taper ratios, and flag when the lift configuration is approaching its operating limit for the prevailing fluid conditions.

It consolidates results across all wells in the active dataset into a single portfolio view. KPI cards show aggregate field totals — combined STOIIP, average water cut, total production and combined NPV. Below that, a colour-coded portfolio table ranks all wells by intervention priority. A plain-language executive verdict summarises the field's overall status and the single highest-priority action.

Each well is assessed against three signals: rate of decline (PHF above 1.1 elevates priority), EOR score (high P_EOR_adj with unaddressed intervention elevates priority), and skin from PTA (skin above 5 elevates priority). Wells meeting two or more criteria appear red. Amber means one criterion. Green means the well is within expected parameters and no immediate intervention is indicated.

Go to the Chemistry tab and scroll to the Live Water Chemistry Calculator. There are two ways to enter data.

Option 1 — Direct TDS entry (fastest): If you have a TDS meter reading from a produced water sample, enter it in the Total Dissolved Solids field at the top of the calculator. The platform immediately classifies the salinity (Fresh below 10,000 mg/L · Moderate 10,000 to 50,000 · High 50,000 to 100,000 · Briny above 100,000) and shows polymer compatibility guidance for that salinity class. WFAR recalculates using your measured TDS as the primary input.

Option 2 — Individual ion entry: Enter calcium, magnesium, bicarbonate, chloride, sulphate and sodium in mg/L, plus reservoir temperature and field pH. TDS is derived automatically from the sum of ions and displayed in the results card. SSI, WFAR, ECCS and scale inhibitor dose all compute instantly as you type.

If you enter both a direct TDS value and individual ions, the direct TDS takes precedence for WFAR and salinity classification. Click Reset to dataset defaults to restore the pre-calibrated ion profile for whichever formation is selected.

The Stiff-Davis method (1952) is the industry standard for calculating the Saturation Scaling Index in oilfield produced water. It improves on the earlier Langelier index by accounting for ionic strength effects in high-salinity brines — which is essential for Indian formation waters, particularly in Ankleshwar and Cambay where TDS routinely exceeds 30,000 mg/L. A positive SSI means the water is supersaturated with calcium carbonate and will deposit scale. The higher the SSI, the more aggressive the scale treatment required before injection.

WFAR is the Waterflood Adversity Rating — a composite index that reflects how hostile the formation water chemistry is to a waterflood or chemical EOR programme. It is computed from TDS, calcium concentration, sulphate concentration and reservoir temperature, each weighted by their relative impact on polymer and surfactant degradation. A WFAR above 0.6 means waterflood is high-risk without pre-treatment. Values below 0.35 indicate favourable chemistry for injection. The Mumbai High carbonate typically shows WFAR below 0.25 — one reason it is the benchmark field for chemical EOR in India.

The Workflow tab chains all six analytical modules into a single integrated decision for the selected well. Step 1 shows reservoir characterisation from Well Intelligence. Step 2 shows pressure diagnosis from PTA/RTA — permeability and skin. Step 3 shows the EOR intervention decision incorporating skin, wettability and chemistry. Steps 4 to 6 cover chemistry validation, production engineering and decline monitoring. The summary at the bottom gives a plain-language recommendation — whether to acid stimulate, proceed with EOR, or simply monitor. Switch wells and the entire chain updates instantly.

Skin factor from pressure transient analysis is one of the most important inputs to EOR decision-making. A well with skin above 5 should not receive a chemical EOR programme until the damage is removed — the injected chemicals will simply bypass the damaged zone near the wellbore. HyperSenseIQ feeds the PTA-derived skin directly into the EOR score modifier, penalising injection methods for damaged wells and flagging acid stimulation as the correct first intervention. This is the integration that most standalone EOR screening tools miss entirely.

HyperSenseIQ uses a shared integration state that all modules write to and read from in real time. When you run PTA/RTA, the skin and permeability are stored and the EOR module immediately applies a damage penalty to injection methods — visible as a "-skin" tag on the score bars. When you run Nodal Analysis, the operating rate and lift requirement are stored and appear in the Workflow summary. When you use the Live Chemistry Calculator, the SSI and scale risk appear as warnings in the Workflow chain. The platform does not just run modules in isolation — it connects them into one continuous intelligence loop.

Yes, directly and automatically. Skin above 5 applies a 20% penalty to injection EOR methods — polymer, ASP, surfactant and waterflood. Skin between 2 and 5 applies a 10% penalty. The penalised methods show a "-skin" tag next to their score. This is physically correct — injecting into a damaged wellbore wastes chemicals and capital. HyperSenseIQ recommends acid stimulation before EOR for any well with skin above 2, and the Workflow summary states this recommendation explicitly. Run PTA first, then EOR Screening — the platform will connect the two automatically.

The platform uses live Brent crude pricing converted to INR at real-time FX rates. It applies GoI royalty rates, OID cess, GST on services, and ONGC or OIL India equity percentages to compute field-specific NPV and IRR. Payback period and undiscounted cash flow are also shown. All fiscal parameters are editable in the Economics tab for sensitivity analysis.

Arps harmonic decline with b = 0.5 as the default, appropriate for most Indian sandstone reservoirs. The platform computes EUR, remaining reserves, abandonment rate and the Production History Factor (PHF) which validates whether the well's actual decline matches the model. A PHF above 1.1 flags accelerated decline for investigation.

The simplest path is two or three anonymous wells from your field — as LAS files, structured data sheets or direct platform entry. HyperSenseIQ runs the full six-layer analysis and delivers a confidential report. No data leaves your organisation. The outputs are anonymised. This is a zero-risk proof of concept before any formal engagement.

The platform operates on an annual SaaS licence model. Pilot engagements are offered at a fixed project fee. Post-pilot, licensing options include per-well annual, per-field annual and enterprise-wide access. Indian PSU pricing is calibrated for DGH budget cycles. Contact niranjanbilgi@yahoo.com for a formal proposal.

The Mining tab is live with architecture for coalfields (BCCL/CIL Jharia) and iron ore (NMDC Bailadila) as flagship demos. The platform applies analogous intelligence — ore grade distribution, depletion curves, MMDR Act fiscal compliance and recovery optimisation. The mining campaign follows the petroleum pilot validation phase. SWIR spectral analysis applies directly to core drill samples from mineral exploration.

It demonstrates the SWIR (Short-Wave Infrared) spectral engine on synthetic core plug data. The chart plots spectral absorption intensity across wavelengths from 1600nm to 2400nm. Characteristic peaks at specific wavelengths identify the fluids and minerals present in the core — oil, brine, clay, dolomite and mica each have diagnostic absorption signatures. The engine cross-checks these spectral identifications against the wireline log interpretation to confirm or challenge the pay zone call.

Each fluid and mineral has characteristic absorption bands. Around 1730nm indicates C-H bond stretching from oil and organic compounds. Around 1900nm indicates O-H bond stretching from water and brine. The 2200nm region identifies clay minerals — kaolinite, illite and smectite all show slightly different peak positions here. Dolomite shows a distinct doublet near 2320nm. Mica appears around 2350nm. The engine maps peaks to mineral identities and flags any disagreement with the log-derived water saturation.

The current platform uses synthetic spectral data that replicates the wavelength response of known mineral assemblages. Real core plug hyperspectral image analysis — where an actual core photograph is processed pixel by pixel — is available for validation engagements. The patent covers this transformer-based image processing pipeline. For pilot clients, real core data from their field can be processed and compared against the log-derived interpretation.

Wireline logs measure bulk formation properties averaged over the logging tool's vertical resolution — typically 0.5 to 2 feet. Core spectral analysis works at millimetre scale on the actual rock sample. Where logs indicate a clean sand with low water saturation, spectral analysis of the core can confirm this with direct mineral and fluid identification. Where there is disagreement — for example, log Sw of 30% but spectral showing strong brine signal — the platform flags the interval as requiring further investigation before a completion decision.

Three categories of resources: data submission forms for requesting a demonstration report (Well Starter Data Form in Excel and PDF), reference datasets for the Material Balance module (7-well CSV and JSON viewer), and platform documentation including the technical explainability note and free vs chargeable services comparison. All items are freely downloadable without registration.

It is a structured data template — available as Excel or PDF — that collects the 15 minimum parameters needed to run a full HyperSenseIQ analysis on your well. Approximately 10 minutes to complete. If you have a LAS wireline file, attach it instead and the reservoir parameters are derived automatically. A completed form or LAS file is all that is needed to receive a confidential demonstration report within 3 to 5 working days at no charge.

It is the anonymised 7-well Material Balance dataset from a 9-month field study — available as a CSV download and viewable as formatted JSON directly in the browser. The dataset includes OOIP, aquifer classification, water influx volumes and drive mechanism indices for all 7 wells, with real well identifiers replaced by pseudonyms. It serves as both a worked example for the Material Balance module and a benchmarking reference for comparing your own field's energy balance against a validated dataset.

Three sub-tabs: Overview (deposit characterisation and ore grade distribution), Volumetrics (in-situ resource estimation using the mining equivalent of STOIIP — tonnage, grade and contained metal), and Recovery (extraction method ranking analogous to EOR screening, adapted for open-cast vs underground vs in-situ recovery methods). A Mining Economics sub-tab applies MMDR Act royalty rates and GoI fiscal parameters to compute NPV and IRR for the mineral asset.

The two flagship reference cases are Jharia coalfield (BCCL/CIL) for coking and thermal coal, and Bailadila iron ore deposit (NMDC) for high-grade haematite. The physics of ore grade distribution, depletion curve modelling and mine life estimation are directly analogous to the petroleum modules — the same decline curve mathematics that applies to well production applies to ore extraction rates. SWIR spectral analysis also applies to core drill samples from mineral exploration, where it identifies oxide vs sulphide mineralogy and gangue content.

It applies royalty rates under the Mines and Minerals (Development and Regulation) Act — iron ore at 15% of average sale price, coal at the rate specified by the state government concerned. District Mineral Foundation and National Mineral Exploration Trust contributions are included. The output is field NPV, IRR and payback period at the current commodity price, with sensitivity sliders for commodity price, production rate and capex. The structure mirrors the petroleum Economics tab so operators familiar with one can immediately read the other.

The Well Integrity tab delivers a five-component integrity screening for every well in the active dataset: Cement Bond Index by zone, Corrosion Risk Assessment, Sustained Casing Pressure classification, Workover Decision Engine, and Annular Barrier Verification. It also scores the entire field portfolio on a single integrity dashboard so you can rank wells by risk and prioritise intervention. All outputs are derived from petrophysical and water chemistry data already in the platform — no separate data upload is required for the screening result.

The Cement Bond Index (BI) quantifies cement quality behind casing. A BI of 1.0 means perfect bond; 0.0 means free pipe. In a CBL/VDL log, BI = 1 − (measured CBL amplitude / free-pipe reference amplitude). In HyperSenseIQ's proxy model, BI is derived from the acoustic transit time deviation from the formation matrix value, combined with porosity and fluid type — higher porosity and oil-bearing zones reduce the bond signal, exactly as a real CBL log behaves. Classifications: BI ≥ 0.80 = Good Bond, 0.55–0.80 = Moderate Bond, below 0.55 = Poor Bond / Channel risk. For direct CBL log interpretation, upload a LAS file containing CBLAMP curves via the Your Well Data tab.

Sustained Casing Pressure (SCP) is pressure that rebuilds in a casing annulus after bleed-down, indicating communication between the annulus and a pressurised source — usually the reservoir or a gas cap. It is one of the most common well integrity failures in mature Indian fields, particularly in high-permeability reservoirs with high water cut. Left unmanaged, SCP can lead to surface casing vent flows, underground blowouts, or uncontrolled releases. HyperSenseIQ classifies SCP risk using permeability, water cut, porosity and scaling tendency as proxies. Category I = manageable with routine surveillance; Category II = schedule diagnostic bleed-down test; Category III = critical, immediate action required. The equivalent Indian standard is OISD-STD-174, which will be reflected in a future platform update.

Every producing well must maintain two independent barrier envelopes between the reservoir and surface at all times — if one fails, the other prevents an uncontrolled release. OISD-STD-174 is the Indian regulatory standard governing well integrity management, mandated by the Directorate General of Hydrocarbons. It defines this two-barrier philosophy identically to NORSOK D-010:2013 §6.2, the internationally recognised Norwegian standard. The Primary Barrier includes the production packer, wellbore fluid column, and perforation cement. The Secondary Barrier is the cement sheath in the casing annulus and the casing string itself. HyperSenseIQ evaluates each of the four barrier elements and flags UNACCEPTABLE wherever Bond Index, corrosion rate, SSI scaling index, H₂S service, or SCP category indicates a potential compromise — with the specific reason shown.

The Workover Decision Engine compares the economic value of remaining producible oil against the cost of intervention. It estimates remaining producible oil from STOIIP, water cut and recovery factor, values it at the current Brent-linked Indian crude price, and compares it against a cement squeeze job cost (approximately ₹85 lakhs for a typical onshore well) and a Plug and Abandon cost (approximately ₹1.2 crore). If the benefit-cost ratio exceeds 5× and the Bond Index indicates channelling risk, it recommends Cement Squeeze. If the ratio is below 5× with poor bond, it flags the well for P&A evaluation. If bond quality is acceptable, it recommends Monitor and Maintain with scheduled surveillance. These cost figures are configurable for each operator's actual AFE rates in a pilot engagement.

When a Norwegian Volve or FORCE 2020 demo well is loaded from the Downloads tab, the Well Integrity tab automatically switches to single-well view mode. The Field Portfolio panel shows only the active demo well with its Indian analog label. The Annular Barrier Verification uses petrophysical proxies from the LAS-derived parameters. The chemistry inputs (SSI, corrosion rate) use Norwegian North Sea analog values with a clear proxy disclaimer. For direct CBL/VDL interpretation on Volve wells, a DLIS-to-LAS Python conversion workflow is available — contact us for the script.