Methods for generating depofacies classifications for subsurface oil or gas reservoirs or fields

Abstract

Described herein are embodiments of a method for generating a refined depofacies classification corresponding to a subsurface reservoir. The method includes analyzing a plurality of rock cores obtained from a plurality of wells drilled in the reservoir or field, analyzing a plurality of well logs comprising a plurality of different well log types obtained from the plurality of wells, and determining an initial depofacies classification for at least portions of the oil or gas reservoir or field. It is then determined whether at least one diagenetic, heavy, light or anomalous mineral is present in some of the analyzed rock cores, and if so, whether at least one well log type from among the plurality of different well log types is capable of substantially accurately identifying the presence of the mineral present. The initial depofacies classification is re-analyzed and reclassified to produce a refined depofacies classification.

Description

technical field [0001] Various embodiments described herein relate to the fields of rock type determination, analysis, and classification in petrophysics, oil and gas reservoir characterization, and the fields of methods and systems associated therewith. Background technique [0002] Prediction of petrophysical facies from well log data (where the predicted petrophysical facies is consistent with the core description) has been an ongoing challenge in the field of petrophysics. For example, multiple iterations of predicted petrophysical facies in an oil or gas reservoir or prospect sometimes do not generate facies that reliably or accurately represent regional stratigraphic continuity. A faithful representation of petrophysical properties in petrophysical facies is required to create static models, facies and permeability estimates that can be used for subsequent dynamic modeling with little or no adjustment. Thus, well log properties and petrophysical facies are key inputs ...

AI Technical Summary

Problems solved by technology

[0005] The foregoing and other factors can lead to: differences in resolution between well logs and cores (which can introduce inconsistencies in resulting petrophysical facies or reservoir models), inconsistencies between facies models created with state-of-the-art techniques and sedimentary sequences, Predicted petrophysical facies that match cores reasonably well but lack sufficient continuity across the field or reservoir model, predicted petrophysical facies with insufficient resolution to allow accurate reservoir modeling, and The velocity model for exhibits random positioning errors and poor or insufficient adjustment to velocity anisotropy

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