Characteristics, Main Controlling Factors and Prediction of Complex Fracture–Cavity Reservoirs of Buried Carbonate Hills in the Weixinan Depression, Western South China Sea

Abstract

Buried hill oil reservoirs have become a key area for offshore oil and gas exploration. In this paper, a typical oil field in the western South China Sea is used as the research object, and a study on the characterization, cause of formation and prediction of the fracture–cavity reservoir distribution is carried out. The reservoir in the study area is a complex fracture–cavity reservoir that developed due to weathering and leaching, tectonic movement and dissolution reconstruction on the limestone skeleton. The reservoir spaces are composed of karst caves, fractures and pores. The main controlling factors include lithological changes, karst landforms, tectonic deformation and faulting. To address the controlling mechanisms of the lithological changes on the formation of fracture–cavity reservoirs, a new parameter, the lithology standard deviation, to evaluate lithological changes is proposed based on the characteristics of the lithological changes, and the distribution of these lithological changes is portrayed in combination with the seismic attributes. The tectonic deformation principal curvature inversion algorithm is used to simulate the distribution of the tectonic principal curvature at the top of the Carboniferous. The larger the tectonic principal curvature is, the stronger the deformation of the rock formation and the more favorable the conditions are for fracture formation. The karst geomorphology controls the overall reservoir distribution, and the karst highlands and karst slope areas are the zones with the most–developed secondary pore space (or fractures and karst caves). The faulting control area is the fracture and dissolution pore development area, major faults control the distribution of the karst cave reservoirs, and secondary faults influence the formation of fractures in the faulted area. The study predicts and evaluates the distribution of fracture–cavity reservoirs from the perspective of fracture–cavity genesis quantification, and by gridding and normalizing the four major genesis quantification evaluation parameters and fusing the geological factors that control the formation of fractures and karst caves by using Back–Propagation neural network deep learning algorithms, a method for predicting the distribution of fracture–cavity reservoirs constrained by geological genesis analysis is developed.