Machine-Learning Techniques Track, Monitor Offshore Water-Treatment KPIs

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206173, “Offshore Water-Treatment KPIs Using Machine-Learning Techniques,” by Lauren Flores, Martin Morles, and Cheng Chen, Chevron, prepared for the 2021 SPE Annual Technical Conference and Exhibition, Dubai, 21–23 September. The paper has not been peer reviewed. New water treatment facilities in the Gulf of Mexico include a seawater sulfate removal unit (SRU) to mitigate reservoir souring and scaling. Current industrial practice relies on only pressure drop and regular cleaning intervals to perform SRU maintenance, which may result in reduced membrane life because of cleaning frequency or severe membrane fouling without the capability to predict fouling based on process conditions. The machine-learning techniques applied in the complete paper aim to deliver a prediction model based on both simulation and real-time field data. The model tracks and monitors system key performance indicators (KPIs). KPI Model Establishment Before KPI models were established in this study, key model input and output parameters were identified. The input parameters were values that could be measured directly, including feed temperature, feed and permeate total dissolved solids (TDS), and feed and reject pressures. The output parameters were predicted membrane pressures, fouling factors, and permeate sulfate concentrations. For membrane fouling monitoring, current industrial practice over-looks the effect of other parameters on pressure drop, which may lead to incorrect decisions. In this study, fouling factor was used to reflect an accurate picture of membrane fouling. Fouling factor is a projection of mem-brane fouling over time, typically with a value between 0 to 1. A fouling factor of 1 indicates that the aged membrane has the same fouling profile as new membrane, while a smaller fouling factor means more-severe fouling. With regard to the SRU process, the major fouling mechanisms causing flux reduction include particle plugging and biofouling. The first step was to obtain SRU data for the prediction model. Because the SRU has not been installed, a membrane-simulation tool was applied to generate performance data under various operating conditions. The next step was to use simulation data for model selection, training, and validation. Several models were evaluated using training data, and the model scores were compared. Metrics were used to determine the model that best fit the data used; however, extensive validation and sensitivity analyses were used to understand the model capability of generalization. Definitions provided in this synopsis are taken from sources documented in the complete paper.