Enhancement of Routine Data Acquisition in a Giant Offshore Brownfield by Bridging Gaps Identified Through Comprehensive Data Analysis

Abstract

Reservoir management is a data driven process with an objective to achieve an optimum ultimate oil recovery. It is fundamental to obtain a proper understanding of well and reservoir performance, which can only be built based on the acquired data. Data acquisition in brownfield has been a significant challenge due to the obsolete control system, accessibility and workflows. Daily well changes is one of the key pieces of data required in routine allocation, well performance analysis, as well as simulation model updates and hence development plans. There are two major types of acquired data in the presented giant offshore brownfield, which are manually measured by operators and automatically recorded data through available SCADA system. A comprehensive data analysis has been conducted based on historical production data and reservoir surveillance data to spot the gaps and identify the opportunities for future improvement. Gaps in daily well changes data have been observed from both manually and automatically acquired data. It has been summarized into two main categories, which are data inaccurate and data missing. The inaccuracies are mainly from improper use of well change event types, inaccurate timing of data acquisition and malfunctioning of SCADA systems. Missing data includes loss of manual measurement records and insufficient utilization of SCADA data. The paper presents real examples of all these findings and a proposed workflow to enhance the data acquisition process. The concise and explicit workflow is one of the most efficient approach to tackle the hardware and manpower limitations. The importance of daily production events could not be over emphasized. Specific actions to bridge the identified gaps are crucial to achieve a sound reservoir management, maintain the sustainability, and ensure an optimum oil recovery.