Integrated reservoir studies aim at synergizing all disciplines to form a reservoir understanding and best strategy to field development. Handling uncertainty and risk using probabilistic approach is a challenge since it becomes quickly overwhelming. This paper describes the approach used to handle uncertainty and risk in the Saudi Aramco Event Solution process. This approach lies in between the conventional deterministic approach where the team agrees on one realization and the probabilistic approach where a whole family of realizations is covering all possible outcomes. This paper describes the innovative Saudi Aramco Event Solution process 6 approach to jointly co-manage and model uncertainty and risk through an integrated static, dynamic and economic field development strategy process. This approach, that lies between a conventional deterministic and a fully probabilistic model approach, provides a range of qualified field development scenarios, under uncertainty and risk, including information and mitigation plans, in a reasonable timeframe (2-3 months).
The purpose of the simulation history match phase in a study is to achieve a simulation model calibrated to historical performance for predictive production forecasting while preserving reservoir understanding in terms of reservoir characterization and fluid flow mechanisms. The classical history match simulation approach involves running a number of history match simulation cases with modified simulation model variables to obtain only one of the many probable match models to the field data. SPE 120958Starting in the 90s, major oil and gas companies engaged in the concept of uncertainty in field exploration, reservoir characterization 5 and development 4 . The scale, variety and complexity of uncertainty factors from seismic to economics, however, has deterred even the most determined to conclude a practical integrated uncertainty and risk modeling solution 6 . Indeed, most known uncertainty management techniques consider only two possible approaches, namely, a deterministic realization to reflect reservoir physics and flow mechanisms with low and high values for all variables (i.e. uncertainty boundaries). Secondly, a probabilistic technique that encompasses all uncertain factors ranges with lesser detailed attention on reservoir physics and understanding. This paper describes the innovative Saudi Aramco Event Solution process approach to jointly manage and model uncertainty and risk through an integrated static, dynamic and economic field development strategy process. This approach that lies between a conventional deterministic and a fully probabilistic model approach, handles uncertainty through a variable elimination and narrowing down methodology. The Event Solution process involves handling uncertainty through many stages (i.e. static modeling, history match, well design and prediction stages). This paper presents the application of an innovated history match approach that provides all project stakeholders with a shared understanding of critical and non critical uncertainties (static and dynamic) in history match as carried forward to prediction under uncertainty forecasting.
The application of Complex Wells (CW) as a component of an optimized field development strategy at single well, sector model and or small scale multi-well level is generally well understood. In contrast, the application, modeling and optimization of a full field strategy inclusive of CW remains a daunting industry challenge. This paper describes a highly successful four-step workflow to manage, assess and model the qualification of a CW optimization strategy at a full field model scale. The paper utilizes a field case to illuminate a CW field wide optimization - extended production plateau, increased production rates and CW completion strategies - approach as compared to single, sector and or multi-well level CW model derived field development decisions. In this paper, complex wells include; Multi-laterals (ML) and Maximum Reservoir Contact wells (MRC) inclusive of down-hole Internal Control Devices (ICD), Equalizers (ICD or equalizers) and or Internal Control Valves (ICV). The workflow starts with reservoir understanding and identifying the need for CW. A sector model is then extracted from the full field model to conduct detailed well level analysis. This is followed by defining well sensitivity cases, that may include a range of field development decision scenarios and a resultant optimization strategy that recommends a combined conventional and CW field optimization approach. From sensitivity cases, the best fit well(s) case scenario is identified. Thereafter, the optimized well(s) strategy is applied to the full field model and evaluated under uncertainty. Detailed analysis is conducted on both the sector and full field models including well location, orientation, placement, length, and target zones for various CW configurations. The resultant development strategy is an optimized full field CW development decision approach including for example; improved sweep efficiency, delayed water breakthrough, improved oil recovery, reduced water handling expense, reduced produced water, extended oil plateau duration and reduced environmental impact. Introduction A field development plan that supports optimized cost, maximize production plateau duration and recovery (maximizing net present value) is a primary target of all studies. Although simply stated, the scale and complexity of most studies presents many challenges to a successful study outcome. For example, the requirement to investigate all possible study decisions and or strategies (e.g., depletion, water injection, etc.), the potential of various well types (i.e., vertical, horizontal, maximum reservoir contact, etc.), the range of potential well completions (e.g., open hole, ICVs, ICDs, etc), alternative optimization tactics (e.g., water injection voidage replacement, well spacing, etc.), and finally the need to consider full study uncertainty analysis and a specific study objective function (e.g., recovery, NPV, etc.). Most attempts to handle the scale and complexity of most projects opt to reduce study complexity by reducing the number of study variables either on the uncertainty or decision side1,2. Such an approach, in our experience, omits critical project decision factors, leading to misleading project decisions and results.
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