Research on Quantitative Inversion Characterization of High-Definition Electrical Imaging Logging in Oil-Based Mud Based on BPNN and MPGA-LM Algorithm

Abstract

Electrical imaging logging in OBM (oil-based mud) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multi-frequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a BPNN (Backpropagation neural network) and the MPGA (multiple population genetic algorithm)-LM (Levenberg-Marquardt) algorithm for high-resolution quantitative imaging is proposed. First, using the theory of physics model-driven approach, numerical simulation is utilized to calculate the well logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of over 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures etc.