As reservoirs mature, subsurface flow complexity and surface production operation challenges increase. This brings the necessity of making capital-intensive decisions to sustain or increase reservoir potential in an optimum way. However, subsurface uncertainties affect decision success. Reservoir surveillance, a process that involves data acquisition, validation, analysis, integration opportunity generation and execution, can mitigate the outcome of such decisions in the presence of uncertainties. Although Value of Information (VOI) is a well-known process for justifying data acquisition, engineers struggle to extract the relevant information from historical data to apply Bayesian approach. The objective of this paper is to illustrate a methodology for identifying the value of information in reservoir management, in particular for deriving the conditional probabilities of success when new and imperfect data are acquired. A methodology to assess the value of reservoir surveillance is supported by two cases. In the first case, the incremental value of Real-Time Reservoir Characterization (RTRC) in underbalanced drilling (UBD) was nearly 100 times the cost of the services; in the second case, the incremental value of permananet downhole gauges (PDHG) was near 230 times the cost of installation and services. Reliability of facquired data, among other uncertainties, resulted to be a key success factor for both cases; however, in worst-case conditions, the incremental value was always positive.
UAE has one of the largest portfolios of reservoir development projects across the world. In managing such projects, ADNOC and its operating assets make multiple decisions to optimize production and recovery factor, by selecting the most adequate exploitation plan including among other, reservoir drainage architecture, production allowables and injection distribution. Reservoir management involves the monitoring, analysis and decision making to optimize business performance results and indicators. ADNOC has envioned strategic goals of reaching sustainable production targets while ascertaining quality assurance through consistency and standardization and by applying best practices. To achieve these strategic goals, ADNOC also identified the following needs, namely: (1) protect the intellectual capital of the workforce (2) train and deploy the growing young workforce faster and consistently, (3) generate the blueprint for consistent workflow execution and (4) provide the platform for expert knowledge sharing and best practices. To achieve these objectives, ADNOC has embraced a framework named Integrated Reservoir Management (IRM) as one of the key management best practices. IRM is a collection of building blocks, processes, and workflows from surveillance strategy to opportunity generation and execution monitoring. The framework also delineates a set of measurements that allows monitoring of reservoir performance management in the operating assets. It also provides a firm basis for connecting the company strategy with those cross-disciplinary processes and workflows in its day-to-day operations. World-class reservoir management processes include the optimum planning and utilization of reservoir surveillance activities, proper data and information integration to reveal reservoir opportunities, proactive decision making and continuous monitoring of performance. In this paper, we discuss the experiences in implementing IRM across ADNOC operating assets during the previous two (2) years. The project started identifying current (or as-is) processes used in the operating companies (OPCOs) and across International Oil Companies (IOCs). Based on the analysis of multiple processes in OPCO, a framework of 6 blocks, 18 processes and 29 workflows was developed. As a result of implementation, IRM framework has allowed sharing standard reservoir management workflows with the added benefits of (a) shortening learning curves to new employees, and hence reducing the total cycle time, resulting in increased productivity, (b) measuring KPI's that cultivate proper behaviors to integrate the right multi-disciplinary efforts in delivering consistent results and (c) fostering a new culture that continuously challenge status quo with consistency.
A giant lean gas reservoir overlying a large oil rim is producing for more than 27 years became under depletion mode without any pressure maintenance. Formation collapse in reservoirs under depletion can cause permeability reduction, completion damage and well failure, reducing or even interrupting production and affecting the ultimate recovery from the reservoir. It is therefore critical to predict any risk in formation collapse. If such risks exist, recommendations are required to optimize reservoir management. Stress measurements were acquired and core analysis were performed in intact rocks area and used for 1D MEM (Mechanical Earth Model) and 3D MEM. 1D MEMs for 10 wells were constructed. Rock mechanical tests were conducted on core samples. 3D MEM was created with 13 interpreted seismic overburden horizons and 105 seismic faults. Four scenarios were performed to identify formation failure during the scheduled production. The worst-case scenario will happen of reservoir depletion, in case of weak formation and reactivated faults. Intensive logging, fracture modelling, coring program across the main fault corridor and RMT (Rock Mechanics Tests) were performed in vertical and horizontal holes across the fault corridor area to fulfil gaps of rock mechanical properties (elastic properties and rock strength) and field stresses. The acquired data were seeds for Lab testing, fracture network analysis and fault characterization which used to update the 3D MEM. Additional Lab tests to fill gaps in rock samples with high porosity (> 30%) were carried out and 1D MEM of 5 more wells were constructed, and the 3D MEM were updated. The 2017 updated 3D MEM eliminates three of the 2013 four scenarios and ended up with one robust scenario that shows better reservoir integrity and very small localized areas of pore collapse in high porosity regions only (> 30%) compare to the previous model. The reservoir can produce under depletion mode with production optimization in areas of expected compaction. Well integrity study and compaction monitoring are also considered to be commentary studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.