Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

Abstract

Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir.