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

GEOPHYSICS Pub Date : 2024-02-09 DOI:10.1190/geo2023-0468.1
Jianshen Gao, Ya-Ni Ma, Liming Jiang, Chunli Lu, Juncheng Shi
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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.
基于 BPNN 和 MPGA-LM 算法的油基泥浆高清电气成像测井定量反演特征研究
在 OBM(油基泥浆)中进行电成像测井已有一段时间,并逐渐在描述深层碳酸盐岩和页岩储层中发挥重要作用。电阻率等储层岩石参数的定量表征是该领域最具创新性的发展之一。这项技术的发展需要处理和解决四个核心问题:参数变化范围广、消除低电阻率地层中的泥饼影响、高电阻率地层中的介电翻滚以及多频介电弥散效应。针对上述问题,提出了联合使用 BPNN(反向传播神经网络)和 MPGA(多群体遗传算法)-LM(Levenberg-Marquardt)算法进行高分辨率定量成像的方法。首先,利用物理模型驱动方法理论,通过数值模拟计算多个参数影响下的测井响应数据,从而建立前向响应数据库。然后,在前向响应数据库中,使用 BPNN 拟合仪器响应函数,以压缩数据量。接着,根据拟合的响应函数,利用 MPGA 优化的 LM 算法,建立了储层岩石电阻率、介电常数和板间距等三个参数的反演方法。结果表明,使用三层 BPNN 可以快速准确地计算 OBM 中的电成像测井响应。单点计算仅需 0.1 毫秒,准确率超过 99%。MPGA-LM 算法具有更强的稳定性和更高的反演精度,单点反演时间仅为 2 毫秒,有助于对 OBM 中的电成像测井进行高清定量描述,这对表征地层结构、区分地层裂缝等具有重要意义。
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