Overview: Reserves/Asset Management (December 2005)
Authors: Hafez Hafez H. 
Published: February 2015

One of the primary goals of the reservoir-modeling and -management process is to enable decisions that maximize the production potential of the reservoir. Among the various existing approaches to accomplish this goal, real-time model-based reservoir management, also known as a “closed-loop” approach, has recently generated significant interest. This methodology entails model-based optimization of reservoir performance under geological uncertainty while incorporating dynamic information in real time, which acts to reduce model uncertainty. The benefits and efficiency of the overall closed-loop approach can be demonstrated through real-time optimization of net present value for the reservoir under different recovery mechanisms, production constraints, and an uncertain reservoir description.
The presence of faults and the associated fractures may significantly influence both flow dynamics and performance of fractured reservoirs under any recovery mechanism. Hence, proper representations of fractures at various scales are imperative for reliable performance prediction and recovery optimization of these reservoirs. Methods are available to construct robust models of a fracture network. A critical step before implementation for field-scale simulation studies is to validate these models by characterizing them in terms of flow properties. Simulators exist for computing the single-phase-flow response of any type of fractured reservoir, whatever the scale, density, and connectivity of the fractures. The potential use of tracer responses to acquire additional information on the distribution of reservoir heterogeneity in general, and of fractures in particular, makes the extension of the capability of these simulators to model tracer tests worthy of consideration.
The digital oil field is gaining attention rapidly within the oil and gas industry. Several oil and gas operators are working to develop their vision for an oil field of the future, testing new technologies, setting up programs, and participating in industry events. The vision is an integrated approach allowing more real-time control of asset management. Many different names are used in the upstream industry to describe this trend, including smart field, digital oil field, next-generation oil field, field of the future, e-field, instrumented field, and intelligent energy. The goal is to improve decision making and, therefore, asset reliability. The method discussed in the third paper emphasizes real-time data management and integration. A key challenge in implementing this approach is the handling of the large volumes of data. The good news is that many of the current tools (e.g., software) need little modification to be adapted to maintain control of the data flow and integration. However, there still is uncertainty regarding what needs to be done and what value it will actually bring to the industry. Several attempts are transitioning from the initial envisioning and abstract phase to projects creating measurable value for the industry.
Available from the SPE eLibrary: www.spe.org
SPE 93160 “Coalbed-Methane Simulator Development for Improved Recovery of Coalbed Methane and CO2 Sequestration,” by E. Syahrial, Lemigas.
SPE 94610 “Price-Uncertainty Quantification Models Advance Project-Economic Evaluations,” by G.T. Olsen, SPE, Devon Energy Corp., et al.
SPE 94838 “Applications of Experimental Design in Reservoir Management of Smart Wells,” by T.E.H. Esmaiel, Delft U. of Technology and Kuwait Inst. for Scientific Research.