Permeability Prediction Using Rock-Typing, Flow Zone Indicator and Machine Learning Techniques in a Brownfield Offshore Malaysia

Abstract

Predicting permeability in low-medium quality reservoirs (> 10 md to <100mD) is important in brownfields since many of them can still produce hydrocarbons. Developing an approach relating geologic properties to permeability prediction can increase field reserves and extend producing life. The common practice of predicting permeability includes linear regressions of core-porosities vs. core-permeabilities applying different lithofacies. However, these methods discount data scattering around regression-lines. This paper describes an innovative-technique for permeability prediction that combines rock-types, flow-zone-indicator (FZI), and machine-learning techniques (ML). FZI is a reservoir-flow-unit that controls hydraulic fluid-flow and is influenced by pore-geometry resulting from diagenetic-processes. In reservoirs, pore-geometry usually is heterogenous due to mineral-composition, rock-texture, cementation, and compaction. Thus,the commonly used permeability equation of Kozeny-Carman (KC) equation still can be used but it needs to be modified for better connecting FZI to hydraulic-flow-units. The modified KC equation incorporates heterogeneous poregeometry as a non-linear-function of porosity by adding cementation-exponent (m) into the equation, where the original KC equation assumes m is equal to one. The semi-log cross-plot between Reservoir-Quality-Index (RQI) vs. PHIZ*Por(m-1) (or FZIm) from the modified KC equation can be constructed using rock-type class. The ML approach was applied to predict FZI groups using 4 standard-logs: gamma-ray, resistivity, density, and neutron-porosity. Cross-plots of RQI vs. PHIZ (conventional FZI) can be compared to RQI vs. PHIZ*Por(m-1) (modified FZI model) usingdata from 11cored wells in oil field offshore Malaysia. The modified FZI model shows less data clustering compared to the conventional FZI model, shown by higher R2 coefficient correlation accuracy. The proposed modified FZI model shows narrower permeability range at low porosity which is a good indication of more accurate hydraulic-flow-unit interpretation. When applying the original and modified FZI models, each lithofacies may occur in more than one hydraulic-flow-unit due to pore-geometry difference within the same lithofacies. Furthermore, the hydraulic-flow-unit generated by the modified FZI model is more sensitive to total porosity when comparing to original FZI model. Each generated hydraulic-flow-unit has better correlation to total porosity and with less scattered permeability at the same porosity. The permeability calculated by modified FZI model was then verified with core permeability showing an excellent overall match. On the ML technique, the "Random Forests" technique will be utilized due to recognized as one of the most recent ML algorithm(s) developed as an innovative technique based on both classifications and regression trees techniques. The Random Forests technique has shown its great accuracy on predictive exactness for these challenge permeability estimations. The prediction quality was benchmark by R2 value of > 0.9 for all crossplots (porosity, permeability, and water saturation) when comparing to routine core analysis lab measurements.