ArticleScientific reports2024
Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning driven reservoir property modeling of the AEB-3E reservoir in the Berenice field Egypt.Scientific reports · 2026Article
- Determining acoustic impedance cube by inverting seismic data using feedforward and radial basis neural networks in an Iranian oilfield.Scientific reports · 2026Article
- Accurate lithofacies identification in deep shale gas reservoirs via an optimized neural network recognition model, Qiongzhusi Formation, southern Sichuan.Scientific reports · 2025Article
- Enhanced petrophysical evaluation through machine learning and well logging data in an Iranian oil field.Scientific reports · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates.
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Registered trials
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