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.
Thinly bedded and laminated shale-sand sequences are very common in most of the producing formations in Iran. Historically, traditionally well logging methods such as conventional resistivity measurement is employed to evaluate pay intervals in order to extract information about hydrocarbon saturation. However, there is the practical matter of resolution of these methods to detect thin laminated shaly sand layers. In order to cope with this limitation, other technique such as: highresolution method should be utilized. For doing this method, we receive low-resolution logs and a high-resolution log from a well logging tool that is disposed in a wellbore which has laminated shaly sand layers. Firstly, square log is produced then these logs are convolved with vertical response function of well logging tools to generate constructed log. The constructed logs are compared with low-resolution logs, if these two logs are matched we choose that square logs as optimum square logs and analyze them. But if these two logs do not match, the square logs undergo interactive refinement then these refined logs are subsequently convolved with a vertical response function of the well logging tool thereby producing a constructed log. Then these constructed logs compare with low-resolution logs. This work repeated until these logs are matched and the optimum square logs are chosen. High-resolution square log method is an iterative method, time consuming and also has very mathematical calculations because we developed a computer program with MATLAB software which can do all these works with high accuracy and low time. Keywords Laminated shaly sand (LSS) • Conventional log • Petrophysical analysis • High-resolution square log method • Convolution • MATLAB software Abbreviations LSS Laminated shaly sand LSSA Laminated shaly sand analysis RHOB Compensated gamma-gamma density average [g/cm 3 ] CGR Compensated gamma-ray [API] NPHI Neutron porosity hydrogen index GUI Graphical user interface NISOC National Iranian South Oil Company
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