Optimizing Data Acquisition and Data Utilization to Maximize the Value of Reservoir Surveillance: Case Studies

Abstract

Abstract Data acquisition is one of the most critical parts to a sound reservoir management across the whole petroleum industry history. There are more challenges in brown fields, especially offshore fields. With the pace of moving to digital oil field (DOF), more surveillance equipment and methods are becoming available. Hence oil recovery could be improved with more accurate reservoir management. Maximizing the utilization of testing equipment and acquired data are becoming more fundamental than ever. This paper presents two field cases from one of the biggest offshore fields. The first case is to maximize the value of available testing equipment through optimizing data acquisition plans. The second case shows a trial of maximizing utilization of the acquired data. As one of the steps moving toward digital oil field, MultiPhase Flow Meter (MPFM) has been installed for more frequent flow tests. In addition to normal flow tests, this paper presents a new role for MPFM, which is to identify communication between strings in dual-string wells. Communication between strings is a high potential risk in this giant offshore field, because majority of the wells are dual strings. Wells with communication between strings have been identified during routine flow tests with no extra cost. In order to assure the feasibility of this approach, conventional communication test through downhole pressure measurement has been performed. Using MPFM to do communication test can save a considerable amount of expenditure comparing to the conventional method. Early actions can also be ensured to avoid reservoir cross-flow. Besides, the volume of communication can also be measured, which could help decide the optimum rate and optimize back allocation. The flow test results are fundamental data for reservoir management and field development. The widely used results are the fluid flow rates. However, the value of temperature data was not fully revealed. The paper presents an empirical correlation derived from measurements. The empirical correlations between WHFT (Well Head Flow Temperature) and flow rate have been generated for each oil production string based on flow test data. The high quality correlations have been utilized to optimize back allocation with conditions. The value of the empirical correlation is more prominent when there are unseen obstructions in the production system. The temperature data in daily operation also indicates abnormal well performance (e.g. DHSV malfunction, scale deposition). Early actions can be planned and taken to avoid further production loss and improve operation efficiency. The two field cases in such a brown field happened in a pre-DOF age, which also shed light on the bright future of digital oil field and better reservoir management.