Theory-Guided Data Science, A Petrophysical Case Study from the Diyab Formation

Abstract

A practical example of a theory-guided data science case study is presented to evaluate the potential of the Diyab formation, an Upper Jurassic interval, source rock of some of the largest reservoirs in the Arabian Peninsula. A workflow base on a three-step approach combining the physics of logging tool response and a probabilistic machine-learning algorithm was undertaken to evaluate four wells of the prospect. At first, a core-calibrated multi-mineral model was established on a concept well for which an extensive suite of logs and core measurements had been acquired. To transfer the knowledge gained from the latter physics-driven interpretation onto the other data-scarce wells, the relationship between the output rock and fluid volumes and their input log responses was then learned by means of a Gaussian Process Regression (GPR). Finally, once trained on the key well, the latter probabilistic algorithm was deployed on the three remaining wells to predict reservoir properties, quantify resource potential and estimate volumetric-related uncertainties. The physics-informed machine-learning approach introduced in this work was found to provide results which matches with the majority of the available core data, while discrepancies could generally be explained by the occurrence of laminations which thickness are under the resolution of nuclear logs. Overall, the GPR approach seems to enable an efficient transfer of knowledge from data-rich key wells to other data-scarce wells. As opposed to a more conventional formation evaluation process which is carried out more independently from the key well, the present approach ensures that the final petrophysical interpretation reflects and benefits from the insights and the physics-driven coherency achieved at key well location.