Data-Driven Management Strategy Can Reduce Environmental Effect of Production Plants
Authors: Carpenter Chris 
Published: July 2019

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195615, “A Data-Driven Management Strategy To Reduce the Environmental Impact of Upstream Production Plants,” by Luca Cadei, SPE, Danilo Loffreno, Giuseppe Camarda, Marco Montini, Gianmarco Rossi, SPE, Piero Fier, Davide Lupica, Andrea Corneo, Lornzo Lancia, Diletta Milana, Marco Carrettoni, and Elisabetta Purlalli, Eni, and Francesco Carduccu and Gustavo Sophia, The Boston Consulting Group GAMMA, prepared for the 2019 SPE Norway One Day Seminar, Bergen, Norway, 14 May. The paper has not been peer reviewed.
This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant. The•developed tool represents a step toward digital transformation of production plants through the integration of data analytics and machine-learning methodologies with•expert domain•knowledge.
Introduction
The tool, called the Energy-Efficiency Predicting and Optimizing Digital-System Tool, was developed as an in-house product for the purpose of helping operators select a series of corrective measures and optimized management actions for an oil and gas production plant. The entire procedure relies on the definition of several key performance indicators (KPIs). The KPIs represent a combination of process parameters useful for understanding, summarizing, and comparing the performance of entire plants or individual equipment units. Comparing the actual KPI vs. past behavior, or a target value, allows operators to understand how the plant and equipment are performing and whether their energy performance can be improved.
Specifically, the tool consists of a machine-learning-based forecasting model and a series of aggregated analytics. The machine-learning model, based on a gradient-boosting regression (GBR) algorithm, predicts the global KPI known as the Stationary-Combustion CO2 Emission Index, allowing operators to estimate future energy efficiency. Along with the forecast, the tool shows aggregated statistics for KPIs of individual equipment units.
Materials and Methods
Case Study. The producing field considered within the current project is on-shore southern Europe. The central processing facility includes five production lines (trains) implemented in separated phases to treat the multiphase flow from•the wells.
The multiphase flow comes from 27 producers and consists of three main phases: gas, oil, and water. The composition of the oil differs according to the formation in which a well has been drilled. This oil has different characteristics from other concessions in Europe, including H2S ranging from 0.5 to 1.5% mol, and CO2 ranging from 5 to 30% mol.
The final scope of the plant is to produce stabilized oil, treated gas, and liquid sulfur, which are then commercialized with the following strict specifications:
The oil is sent by means of a 100-km-long pipeline to a refinery.
The gas is sold to the national gas grid, managed by a third party.
The liquid sulfur, with a purity of 99.9%, is sold to the pharmaceutical and explosives industries.
These energy conversions generate CO2 emissions from the stationary combustion of fuel gas.
In accordance with the Paris Agreement charge to limit the rise in global temperature to below 2°C compared with preindustrial levels, reducing CO2 emissions from this asset is critical. Real-time monitoring and prediction can help meet•this goal.