Artificial Intelligence Transforms Offshore Analog Fields Into Digital Fields
Authors: Carpenter Chris 
Published: December 2019

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195027, “Turning an Offshore Analog Field to Digital Using Artificial Intelligence,” by Roberto Espinoza, SPE, Dragon Oil, and Jimmy Thatcher and Morgan Eldred, Digital Energy, prepared for the 2019 SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 18–21 March. The paper has not been peer reviewed.
Currently, no cost-effective method exists to efficiently, reliably, and accurately capture analog meter readings in a digital format. This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability. This solution was implemented in the Cheleken oil field in the Caspian Sea offshore Turkmenistan. During the field trial, operators were required to take pictures of the gauges at given intervals and upload the photos to the application. After an innovative process of calibration, the acquired images were processed using artificial intelligence and deep-learning computer techniques.
Introduction
Digitizing an oil field is an exciting but costly exercise that requires close supervision to avoid inefficiency. Best results are achieved when priorities and objectives are defined early in the project. For instance, if the objective is cost savings, then digitization should be applied in a manner different from that used in production optimization. Regardless of the objective, implementation of the digital oil field is a constant battle against cost and time.
The most effective value is achieved without 100% digitalization. Often this fact is overlooked, and during the digitization of the field, extra cost is incurred without tangible improvements in the value because the oil field is not homogeneous and not all parameters need to be monitored or optimized with digital devices. For instance, 90% of production comes from 10–20% of the wells in most fields. In digitizing by priority, early value can be brought to the project as stakeholders are encouraged to grow the digitization effort from a monitoring perspective to value maximization.
Automating Input Data
A user able to predict production has the ability to maximize future revenue. However, continuous metering of production is very costly. Installing a multiphase meter on each well is a luxury afforded in only a few fields with very high production. Instead, most fields need to share metering facilities by using a test manifold. Wells are then tested with a certain frequency and the daily rate is estimated on the basis of these tests. Commonly, well production between tests is assumed to be constant until a new test is conducted. This assumption can be improved with the use of a calculated value from a virtual flowmeter (VFM).
One of the more sensitive input parameters for this VFM is the wellhead pressure. In some cases, when the wellhead pressure is measured manually by observing analog gauges, human errors are incurred (in terms of visual, typographical, and duplication errors).
The methodology used in this paper eliminates all human errors while reducing the time and cost of acquisition when acquiring pressure and temperature readings from analog gauges by using computer vision. These improvements in the input parameters resulted in a more-reliable prediction of production rates from the VFM.