Abstract
Fractures, which are related to tectonic activity and lithology, have a significant impact on the storage and production of oil and gas in shales. To analyze the impact of lithological factors on fracture development in shales, we selected the shale formation from the Da’anzhai member of the lower Jurassic shales in a weak tectonic deformation zone in the Sichuan Basin. We defined a lithology combination index (LCI), that is, an attribute quantity value of some length artificially defined by exploring the lithology combination. LCI contains information on shale content at a certain depth, the number of layers in a fixed length (lithology window), and the shale content in the lithology window. Fracture porosity is the percentage of pore volume to the apparent volume of the rock. In the experiment, fracture porosity was obtained using 50 samples from six wells, by observing rock slices under a microscope. The relationship between LCI and fracture porosity was analyzed based on machine learning, regression analysis, and weighting methods. The results show that LCI is able to represent the impact of multiple lithological factors (i.e., shale content at a certain depth, the number of layers in the lithology window, and the shale content in the lithology window). The LCI within a thickness of 2 m for the lithology window demonstrates a good linear relationship with fracture porosity. We therefore suggest LCI be used for fracture predictions of shale formations from weak tectonic deformation zones. Our proposed LCI and fracture prediction methods also provide implications for sandstone, mudstone, or carbonate formations under similar processes.
Highlights
Past tectonic activity and lithology are the main controlling factors for the development of opening-mode fractures in shales [1,2,3]
Our study introduces the concept of lithology combinations to represent various lithological factors that could affect fractures and demonstrates the impact that these lithological parameters impose on fracture development
Where I denotes the lithology combination index (LCI), whose value directly represents fracture development; x1 –x3 refers to the three processed composite functions; and z1, z2, and z3 denote the shale content at a certain depth, the number x1 =
Summary
Past tectonic activity and lithology (governed by depositional lithology and diagenesis) are the main controlling factors for the development of opening-mode fractures in shales [1,2,3]. Ouenes et al (1995) introduced a new method, using examples of real reservoirs to characterize and simulate fractured reservoirs by integrating geomechanics, geology, and reservoir engineering [18] They applied neural networks to identify relationships among the reservoir structure, reservoir thickness, and well dynamics, which can be used as indicators of influencing fracture development. Ouenes (2000) examined the related factors that influence fractures in reservoirs, using fuzzy logic and neural networks [19] Based on these techniques, he input fracture-related data, such as the rock mechanical performance index, pressure index, and paleo-stress index in his model, to demonstrate the impact that each factor has on a fracture. The most crucial step in their study was to create a model using a reverse neural network, to quantize and analyze the potentially complex relationship between various geological factors and fracture strength This model was used to predict fractures and identify oil and gas reservoirs. From a geology and petrology perspective, how to scientifically understand and manage these lithology factors is the crucial message of our study
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