基于BP神经网络的低渗透油田开发动态预测
Dynamic Prediction of Low Permeability Oilfield Development Based on BP Neural Network
陈宇家 1王巍 1任利剑 1王兵 1王润萍 1杨军 1樊嘉伟 2朱玉双2
作者信息
- 1. 中国石油玉门油田分公司,甘肃酒泉 735019
- 2. 大陆动力学国家重点实验室/西北大学地质学系,陕西西安 710069
- 折叠
摘要
油藏开发动态预测是制定和调整开发方式的基本依据,为克服传统油藏数值模拟方法过度依赖三维地质模型和渗流机制的可靠性,采用BP神经网络(ANN)方法,在研究区油藏精确描述工作及生产动态分析的基础上,选取了包括地质因素和开发因素在内的12类参数作为基本数据集,通过参数相关度分析、主控因素优选,建立了研究区单井年累计产液和平均含水率的预测模型.根据模型计算结果,研究区产液及产油能力逐渐下降,含水呈逐步上升趋势,2年后区块预测累计产液13.7×104 m3,同比下降25.6％;预测年累计产油量4.7×104 t,综合递减率为31.9％;2年后区块平均含水率达到58.1％,局部区域油井出现水淹情况,含水率突破98％.研究表明,BP神经网络具有简单高效、误差较小等优势,降低了油田开发动态预测的难度和工作量,适用于样本数据集较大的油田开发问题,为低渗透油田的开发动态预测提供了一种新的视角.
Abstract
Reservoir development dynamic prediction is the fundamental basis for formulating and adjusting the development method. In order to overcome the accuracy of the traditional reservoir numerical simulation method that overly relies on the three-dimensional geological model and seepage mechanism, the BP neural network (ANN) method is adopted, based on the accurate reservoir description and production dynamic analysis in the study area. Twelve types of parameters including geological factors and development factors are selected as the basic dataset. Based on the analysis of parameter correlation and the selection of main control factors, the prediction model of annual cumulative fluid production and average water content of a single well in the study area was established. According to the results of the model, the liquid and oil production capacity of the study area gradually declined, and the water content showed a gradual upward trend, and the predicted cumulative liquid production of the block after two years was 13.7×104 m3, with a year-on-year decrease of 25.6％; the predicted annual cumulative oil production was 4.7×104 t, with a comprehensive decreasing rate of 31.9％; and the average water content rate of the block after two years was 58.1％, and the local wells in the area showed flooding, and the water content rate exceeded 98％. This study shows that the BP neural network has the advantages of simplicity, efficiency, and small error, which reduces the difficulty and workload of predicting the development dynamics of oilfields. It is suitable for the problem of oilfield development with a large sample dataset, and provides a new perspective for understanding the development dynamics of low-permeability oilfields.
关键词
开发动态预测/低渗透油藏/BP神经网络/多变量预测/注水开发Key words
development dynamic prediction/low permeability reservoir/BP neural network/multivariate prediction/water injection development引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据