Dept. of Civil Engineering, New Mexico State University, Las Cruces, NM, United States.
Dept. of Computer Science, New Mexico State University, Las Cruces, NM, United States.
Sci Total Environ. 2021 Dec 20;801:149693. doi: 10.1016/j.scitotenv.2021.149693. Epub 2021 Aug 18.
Appropriate produced water (PW) management is critical for oil and gas industry. Understanding PW quantity and quality trends for one well or all similar wells in one region would significantly assist operators, regulators, and water treatment/disposal companies in optimizing PW management. In this research, historical PW quantity and quality data in the New Mexico portion (NM) of the Permian Basin from 1995 to 2019 was collected, pre-processed, and analyzed to understand the distribution, trend and characteristics of PW production for potential beneficial use. Various machine learning algorithms were applied to predict PW quantity for different types of oil and gas wells. Both linear and non-linear regression approaches were used to conduct the analysis. The prediction results from five-fold cross-validation showed that the Random Forest Regression model reported high prediction accuracy. The AutoRegressive Integrated Moving Average model showed good results for predicting PW volume in time series. The water quality analysis results showed that the PW samples from the Delaware and Artesia Formations (mostly from conventional wells) had the highest and the lowest average total dissolved solids concentrations of 194,535 mg/L and 100,036 mg/L, respectively. This study is the first research that comprehensively analyzed and predicted PW quantity and quality in the NM-Permian Basin. The results can be used to develop a geospatial metrics analysis or facilitate system modeling to identify the potential opportunities and challenges of PW management alternatives within and outside oil and gas industry. The machine learning techniques developed in this study are generic and can be applied to other basins to predict PW quantity and quality.
采出水（PW）的妥善管理对石油和天然气行业至关重要。了解一口井或一个地区内所有类似井的 PW 数量和质量趋势，将极大地帮助运营商、监管机构和水处理/处置公司优化 PW 管理。在这项研究中，收集了 1995 年至 2019 年新墨西哥州（NM）二叠纪盆地的历史 PW 数量和质量数据，对其进行预处理和分析，以了解 PW 生产的分布、趋势和特征，以便进行潜在的有益利用。应用了各种机器学习算法来预测不同类型的油井和气井的 PW 产量。采用线性和非线性回归方法进行分析。五折交叉验证的预测结果表明，随机森林回归模型报告了较高的预测精度。自回归积分移动平均模型在时间序列中预测 PW 体积方面表现出良好的效果。水质分析结果表明，来自德拉威尔和阿蒂西亚地层（主要来自常规井）的 PW 样本的总溶解固体浓度最高和最低，平均值分别为 194535mg/L 和 100036mg/L。本研究首次全面分析和预测了 NM-二叠纪盆地的 PW 数量和质量。研究结果可用于开发地理空间指标分析或促进系统建模，以识别石油和天然气行业内外 PW 管理替代方案的潜在机会和挑战。本研究中开发的机器学习技术具有通用性，可应用于其他盆地来预测 PW 数量和质量。
