Abstract

AbstractFractures play a vital role in reservoir transformation, but the distribution of faults and fractures is difficult to obtain by observing cores, which ultimately limits the effective development of gas dolomite reservoirs. We propose an integrated method that incorporates thin-section observations, three-dimensional (3D) seismic data, and image logs to interpret the distribution of faults and fractures of Cambrian Longwangmiao Carbonate Formation to predict potential development areas in the Moxi-Gaoshiti area of the Sichuan Basin, South China. Firstly, the faults were well interpreted by using the automatic tracking and 3D visualization technique based on the new seismic combination attribute of symmetry and ant tracking. Secondly, a comprehensive analysis was conducted using the thin sections, paleogeomorphology, and in situ test results to determine the fracture types (corrosion and structural fractures). The results help us to find potential sweet spot zones with good permeabilities, which is of great significance in reducing the risk of water production of drilled wells in the field development.

Highlights

  • The gas reservoir of the Lower Cambrian Longwangmiao Formation (LWM Fm) in the Moxi area, Sichuan Basin, South China, is the largest mono-block gas field, with proven geological gas reserves of 4404 × 1­ 08 ­m3 (Jin et al 2014; Liu et al 2014; Tian et al 2015; Yang et al 2015; Zhang et al 2015)

  • Symmetry analysis is a new type of seismic volumetric attribute used to measure the degree of chaos in seismic data, which is sensitive to the seismic amplitude variations

  • We studied the characteristics of the two fracture zones through the comprehensive analysis of thin sections, well logs, and paleogeomorphology

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Summary

Introduction

The gas reservoir of the Lower Cambrian Longwangmiao Formation (LWM Fm) in the Moxi area, Sichuan Basin, South China, is the largest mono-block gas field, with proven geological gas reserves of 4404 × 1­ 08 ­m3 (Jin et al 2014; Liu et al 2014; Tian et al 2015; Yang et al 2015; Zhang et al 2015). Recent studies have presented a workflow for characterizing faults and fractures using multi-source borehole data (Van Der Voet et al 2020) and multi-attribute analysis (Ashraf et al 2020a, 2019, 2021). Observations, descriptions, and statistical analysis of outcrops and thin sections in the field are the most direct and effective way to characterize fractures. Fractures can be detected from conventional well logs and image logs with higher vertical resolutions (Ameen, 2014; Lai et al 2018; Radwan et al 2021b, 2021c). In this study, we incorporated the 3D seismic data, well-log data, image logs, cores, and geology data (Anees et al 2019; Ashraf et al 2020b). The information from the literature regarding the fractures and faults was incorporated to strengthen our results

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