The research, conducted by a team from GEOPIC, the research arm of Oil and Natural Gas Corporation in Dehradun, along with collaborators at Pandit Deendayal Energy University and Maharashtra Institute of Technology World Peace University, focused on the gas-bearing tight sandstones of the Jhuran Formation. These rocks, deposited during the syn-rift and early post-rift phases that followed the fragmentation of Gondwana, are anything but uniform. Permeability values range from a near-impermeable 0.01 millidarcies to a still-modest 10 millidarcies, and the formation is sliced into parallel fault blocks by northwest-southeast trending structures that complicate any attempt to extrapolate reservoir quality between wells. For an emerging hydrocarbon province like the Kachchh Offshore Basin, that heterogeneity is a formidable barrier to exploration.

The conventional toolkit offers little comfort. Classic permeability estimation leans on empirical porosity-permeability transforms calibrated to laboratory core measurements. In homogeneous reservoirs such relationships perform adequately, but in tight, clay-rich sandstones the link between porosity and permeability weakens dramatically, because flow is governed by pore geometry, grain packing, clay distribution and cementation rather than by pore volume alone. Compounding the problem, core plugs are expensive and sparse; in this study, direct core measurements existed for just one well, and only fifteen of them. The team therefore turned to machine learning, training four supervised algorithms on wireline log data from four wells: Random Forest, Support Vector Regression, a Multi-Layer Perceptron neural network, and Extreme Gradient Boosting.

The input features were the workhorses of petrophysics: gamma ray, deep resistivity, bulk density, neutron porosity and sonic transit time, supplemented by engineered variables such as volume of clay, total and effective porosity, and a density-neutron-resistivity combination parameter. Nuclear magnetic resonance logging provided an independent permeability benchmark in two wells, derived through the Schlumberger-Doll Research equation, which relates permeability to porosity and the logarithmic mean of the NMR T2 relaxation distribution. Crucially, the NMR-derived permeability served only as the target variable, never as an input, a design choice that prevents data leakage and ensures the models can be applied to wells lacking NMR coverage. Spearman rank correlation analysis confirmed that no input pair approached the severe multicollinearity threshold, supporting their joint inclusion.

Validation was deliberately stringent. Wells A, C and D supplied the training data, split 70-30 into training and testing subsets, with five-fold cross-validation used for hyperparameter tuning. Well B, which contained the fifteen core measurements, was held out entirely and used only once, after model finalisation, as a blind validation well. On that single held-out well, Random Forest achieved a coefficient of determination of 0.77 against the core and NMR benchmarks, the best cross-well generalisation of the four algorithms, and posted the lowest prediction variance, with a residual standard deviation of 1.41. Support Vector Regression, by contrast, fit its training data beautifully, reaching a training R-squared of 0.92, but its validation performance collapsed, the classic signature of overfitting.

The contrast between algorithms carried a practical lesson. The Multi-Layer Perceptron and XGBoost both achieved near-perfect alignment on training data yet generalised poorly, with XGBoost showing the largest gap between training and blind-validation performance and the highest prediction uncertainty in Well A, where residual standard deviations reached 7.81. Random Forest, averaging hundreds of decision trees each trained on random subsets of the data, proved the most stable performer across all wells, with residual standard deviations as low as 0.38 in Well C. The authors suggest a heuristic for similarly data-starved settings: Random Forest as the default choice, Support Vector Regression where smooth low-variance surfaces are needed, and neural networks or gradient boosting only with strong regularisation and blind-well checks, or once more wells become available.

What elevates the study beyond a routine algorithm bake-off is its use of SHAP, or Shapley Additive Explanations, a technique borrowed from game theory that decomposes each prediction into the contributions of individual input features. The SHAP analysis revealed a clear hierarchy of geological control. Effective porosity exerted the strongest first-order influence on permeability, followed by sonic transit time, which acts as a proxy for compaction. Most strikingly, shale volume displayed strongly negative SHAP values beyond a threshold of roughly 35 to 40 percent, marking a permeability collapse in clay-rich intervals. In other words, permeability in the Jhuran sandstones is governed by nonlinear interactions among shale content, porosity and compaction, not by porosity alone, precisely the kind of behaviour that linear empirical transforms cannot capture.

The team was candid about the limits of its evidence base. Four wells with uneven log and core availability is a small dataset, and the NMR calibration constants, drawn from standard literature values for water-wet clastic systems, were checked only qualitatively against the fifteen core samples from Well B rather than formally recalibrated for the Jhuran Formation. The reported R-squared of 0.77 reflects a single blind well, and the authors explicitly caution that basin-wide generalisation requires confirmation with additional held-out wells and more core control. Well D, lacking both core and NMR data, relied on a facies-derived pseudo-benchmark, an independent consistency check but not a direct measurement. These caveats frame the work honestly as a proof of concept rather than a finished predictive product.

Even so, the implications reach well beyond one basin. Frontier exploration provinces worldwide face the same predicament: geologically complex reservoirs, a handful of wells, and sparse core data, all while drilling decisions carry enormous financial risk. The study demonstrates that a transparent, reproducible machine learning workflow, built on open-source libraries and validated against independent physical measurements, can extract usable permeability predictions from ordinary wireline logs under exactly those constraints. By pairing ensemble learning with explainability, the researchers have shown that machine learning need not be a black box; it can double as an analytical instrument that tells geoscientists which geological processes matter most. As exploration accelerates along India’s northwestern continental margin, that combination of prediction and insight may prove as valuable as the predictions themselves, offering a transferable template for characterising tight reservoirs wherever data is scarce and heterogeneity is the rule.

Subject of Research: Interpretable machine learning prediction of permeability in tight Jhuran Formation sandstones of the Kachchh Offshore Basin, India

Article Title: Data driven permeability modelling in tight Jhuran formation sandstones of the Kutch offshore Basin India

Article References: Jyotsna, G. D. D., Anees, M., Mavani, S. H., Thakur, S., Saxena, A., Singh, S. K., Desai, D. B. S. G., & Singh, D. A. (2026). Data driven permeability modelling in tight Jhuran formation sandstones of the Kutch offshore Basin India. Discover Geoscience, 4(1), Article 366. https://doi.org/10.1007/s44288-026-00724-x

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00724-x

Keywords: permeability prediction, machine learning, tight sandstone, Jhuran Formation, Kachchh Offshore Basin, Random Forest, SHAP analysis, well logging, NMR logging, reservoir characterization, petrophysics, explainable AI