New insights into permeability determination by coupling Stoneley wave propagation and conventional petrophysical logs in carbonate oil reservoirs

Abstract

The need to determine permeability at different stages of evaluation, completion, optimization of Enhanced Oil Recovery (EOR) operations, and reservoir modeling and management is reflected. Therefore, various methods with distinct efficiency for the evaluation of permeability have been proposed by engineers and petroleum geologists. The oil industry uses acoustic and Nuclear Magnetic Resonance (NMR) loggings extensively to determine permeability quantitatively. However, because the number of available NMR logs is not enough and there is a significant difficulty in their interpreting and evaluation, the use of acoustic logs to determine the permeability has become very important. Direct, continuous, and in-reservoir condition estimation of permeability is a unique feature of the Stoneley waves analysis as an acoustic technique. In this study, five intelligent mathematical methods, including Adaptive Network-Based Fuzzy Inference System (ANFIS), Least-Square Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Committee Machine Intelligent System (CMIS), have been performed for calculating permeability in terms of Stoneley and shear waves travel-time, effective porosity, bulk density and lithological data in one of the naturally-fractured and low-porosity carbonate reservoirs located in the Southwest of Iran. Intelligent models have been improved with three popular optimization algorithms, including Coupled Simulated Annealing (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Among the developed models, the CMIS is the most accurate intelligent model for permeability forecast as compared to the core permeability data with a determination coefficient (R2) of 0.87 and an average absolute deviation (AAD) of 3.7. Comparing the CMIS method with the NMR techniques (i.e., Timur-Coates and Schlumberger-Doll-Research (SDR)), the superiority of the Stoneley method is demonstrated. With this model, diverse types of fractures in carbonate formations can be easily identified. As a result, it can be claimed that the models presented in this study are of great value to petrophysicists and petroleum engineers working on reservoir simulation and well completion.

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

“…It was worth focusing on the methodology, and presented results to explain many details, necessary to understand the application of fuzzy logic in fracture determination. Methods utilizing computer modeling, deep learning, and artificial intelligence in combination with laboratory and well logging data are still developed to improve permeability determination [99]. In our opinion, and in the opinions of the other above cited authors, the novelty in fractured carbonates interpretation is using the machine deep learning techniques, and artificial intelligence to combine the super detailed data from X-ray computer tomography, and similar sophisticated laboratory technologies with continuous well logging measurements.…”

Prediction Analysis of Fault Mode Sets for High Permeability Distributed Energy Distribution Networks Based on Multivariate Data

“…Rostami, A., et, al. [24] have discussed This work suggests a novel method for figuring out permeability in carbonate oil reservoirs by combining traditional petrophysical data with Stoneley wave propagation. When compared to conventional methodologies, this approach can yield more precise and dependable data, enabling better reservoir characterisation and production predictions.…”

Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs

“…So in 2020, about 9 thousand operations were performed, while additional oil production per well amounted to 0.3 to 1.6 thousand tons. Due to flow-diverting technologies, companies produced over 9.5 million tons of oil, which is about 8% of the total share of additional cash income for 2020 [31,41].…”

“…The sizes of molecules of the selected chemical compounds are comparable with the sizes of high permeability channels. Under the influence of temperature and interaction with the surface of the pore channels, they form a viscous immobile or inactive substance, which, with an increase in pressure at the bottom of the injection well, practically does not move and does not allow water to move along them [41]. This solution does not penetrate into low-permeability channels.…”