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Rock physics-based analysis to discriminate lithology and pore fluid saturation of carbonate reservoirs: a case study

Makarian Esmael, Elyasi Ayub, Moghadam Rasoul Hamidzadeh, Khoramian Reza, Namazifard Pedram

Acta Geophysica

2023

Vol. 71, no. 5

2163--2180

Rock physics templates (RPT) and modeling contribute significantly to accurately and fast characterization of hydrocarbon reservoirs. This study strives to characterize lithology and pore fluid saturation for a carbonate reservoir in southwest Iran using RPT by well-logging data in a zone with a thickness of almost 100 m. To discriminate lithology, Greenberg–Castagna and Gardner models were applied. Also, velocity ratio - Gamma-ray (GR) and density - GR templates were implemented to investigate more. Results show that the researched zone’s lithology consists of a considerable amount of limestone, followed by dolomite, and a small amount of shale, without sandstone, which matches excellently with the lithology column and geophysical logs. For fluid discrimination, two different rock physical directions were impalement. Firstly, the rock physics model of the study area was built through Xu and Payne’s model. Then, velocity ratio - acoustic impedance template (Vsub>p/Vsub>s–AI) was applied to modeled data scaled with resistivity and porosity and then successfully validated with oil and water saturation. Findings show that PRT organizes data concerning similar features (here, aspect ratio), causing easy, fast, and more accurate analysis, and fluid content in the study includes oil and water, which the figure for oil is much more. In different oil and water saturation, non-modeled data were investigated through Vsub>p/Vsub>s − Vsub>s, Vsub>s − Vsub>p, and shear impudence (SI) - AI template supported by pore-pressure (PP) information to further research fluid distribution and its effect in the second direction. Regarding the rock physical analysis, the main reason for the decrease in seismic velocities and impedances is high pore pressure due to high oil saturation.

Lithology identification technology using BP neural network based on XRF

Wang Qingshan, Zhang Xiongjie, Tang Bin, Ma Yingjie, Xing Jisheng, Liu Longfeng

Acta Geophysica

2021

Vol. 69, no. 6

2231--2240

The element content obtained by X-ray fluorescence (XRF) mud-logging is mainly used to determine mineral content and identify lithology. This work has been developed to identify dolomite, granitic gneiss, granite, limestone, trachyte, and rhyolite from two wells in Nei Mongol of China using back propagation neural network (BPNN) model based on the element content of drill cuttings by XRF analysis. Neural network evaluation system was constructed for objective performance judgment based on Accuracy, Kappa, Recall and training speed, and BPNN for lithology identification was established and optimized by limiting the number of nodes in the hidden layer to a small range. Meanwhile, six basic elements that can be used for fuzzy identification were determined by cross plot and four sensitive elements were proposed based on the existing research, both of which were combined to establish sixteen test schemes. A large number of tests are performed to explore the best element combination, and the result of experiments indicate that the improved combination has obvious advantages in identification performance and training speed. The author’s pioneer work has contributed to the neural network evaluation system for lithology identification and the optimization of input elements based on BPNN.