Improving FDP decisions under sparse data: An adaptive metropolis approach for net-pay quantiles
Abstract
Early field-development decisions often rely on very small samples of reservoir property measurements, where common spreadsheet workflows (typically lognormal fits) become fragile and analyst-dependent. This paper presents the Metropolis-Adaptive Distribution Range-Constrained (M-ADRC) method, a practical Metropolis-Hastings (MH) approach with adaptive constraints for generating representative synthetic populations from low number of observations. The workflow combines (i) domain-informed parameter bounds, (ii) adaptive proposal tuning, (iii) automatic switching between Pearson Type III and lognormal distribution families, and (iv) adaptive iteration policy for samples with extreme bias.
Using a Southeast Asia gas field netpay dataset, the representative population generated by M-ADRC is benchmarked against the traditional lognormal spreadsheet approach across diverse net pay samples. Results show that M-ADRC achieves significantly lower Wasserstein Distance and smaller Swanson’s mean error, reaching the “excellent” Wasserstein threshold with roughly eight samples, only half the sample requirement of the spreadsheet approach.
The workflow is lightweight, fully open-source, and requires no specialized hardware. It includes default settings, diagnostic checks, and sensitivity results, enabling reservoir engineers to apply M-ADRC without specialized tuning expertise. M-ADRC yields more reliable small-sample quantiles, reducing the risk of biased inputs in FDP scenarios and enhancing decision confidence. Workflow limitations such as restricted distribution types, resilience to extreme sampling bias, and the lack of geospatial properties will be addressed in future work.
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