Work Flow, Advisory System Optimizes Intelligent Production Network

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205735, “A Digital Oil Field Comprehensive Study: Automated Intelligent Production Network Optimization,” by Aulia A. Naufal, SPE, and Sabrina Metra, Schlumberger. The paper has not been peer reviewed. _ Leveraging technologies from a flow-assurance simulator, a Python application programming interface (API) toolkit, open-source machine-learning (ML) packages in Python, and a commercial visualization dashboard, the complete paper proposes a series of work flows to simplify model deployment and set up an automatic advisory system to provide insight as a means of justifying day-to-day engineering decisions. The complete paper discusses this methodology and provides a case study of its implementation, although this synopsis concentrates on the former. Background The authors identify four steps necessary for a valid network model that can describe production-system fluid-flow behavior accurately and support day-to-day decision making. These four steps, detailed in the complete paper, include the following: - Well-model building and matching - Well-basis gas lift evaluation - Network modeling - Gas-lift-optimization scenarios Work Flow Objective The objectives of creating an automated, intelligent production-network-optimization work flow include the following: - Minimize risk for data-quality issues caused by human error - Quicken pre- and post-deployment stages of the work flow, thus saving time and resources - Increase the functionality of a digital-oilfield work flow, including adding a production-surveillance dashboard, extracting publicly available temperature data from a weather-service API, and adding an ML autocalibration work flow Theoretical Basis Multiphase Steady-State Flow Simulator. A commercial flow simulator is used to simulate multiphase flow simulation throughout the production system from the reservoir to the surface facilities to enable comprehensive production- (and injection-) system analysis. By the term “steady state,” the authors mean that the mass flow rate is conserved throughout the system, with no accumulation of mass in any system component. This simulator is used from design to the optimization period of the oil, gas, and water production and injection systems. It is most often used by reservoir, production, and facilities engineers to model well performance, design artificial-lift systems, model pipeline networks and facilities, analyze field-development plans, and optimize production. Network modeling is necessary if the interaction of different components (such as compressors, separators, and wells) producing into a common gathering system must be considered (Fig. 1); the wellhead pressure and the deliverability of any well are influenced by the backpressure imposed by the production system. Network modeling also allows engineers to determine the effects of adding new wells and compression, looping flowlines, and changing separation systems. To analyze all components, the network should be solved based on the boundary conditions set on each component. The network will be converged when the pressure balance and mass balance at each node are within the specified tolerance. Then, parameters along the flow path (profile) and at each node in the network will be output. The authors discuss simulation settings, a network optimizer, and a flow-simulation Python toolkit in the complete paper.