Numerical Simulation and Multiple Realizations for Sensitivity Study of Shale Gas Reservoirs
- Amirmasoud Kalantari Dahaghi (West Virginia University) | Shahab D. Mohaghegh (West Virginia University)
- Document ID
- Society of Petroleum Engineers
- SPE Production and Operations Symposium, 27-29 March, Oklahoma City, Oklahoma, USA
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.5.2 Core Analysis, 4.3.4 Scale, 5.8.2 Shale Gas, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 2.2.2 Perforating, 5.5 Reservoir Simulation, 5.6.1 Open hole/cased hole log analysis, 2.5.1 Fracture design and containment, 5.5.3 Scaling Methods, 5.5.8 History Matching, 5.1.2 Faults and Fracture Characterisation, 1.6 Drilling Operations, 5.1.1 Exploration, Development, Structural Geology, 6.1.5 Human Resources, Competence and Training, 7.6.4 Data Mining, 7.6.6 Artificial Intelligence, 5.1.5 Geologic Modeling
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Shale gas in the United States went almost instantly from a practically invisible resource to massive reserves that challenge the largest conventional gas accumulations in the world. Shale gas success is directly the result of economically managed deployment of petroleum technology, namely horizontal wells .Horizontal drilling and multi-stage stimulation technologies are driving the successful development of shale plays.
Modeling and simulation of shale gas reservoirs poses a unique problem. The geological complexity of shale gas reservoirs—containing both natural and hydraulic fractures—makes accurate modeling a significant challenge. To overcome these challenges and maximize recovery of a shale gas field requires specialized methods and state-of-the-art technology.
In the first part of this paper an integrated workflow, which demonstrates a quantitative platform for shale gas production optimization through capturing the essential characteristics of shale gas reservoirs was discussed. A comprehensive sensitivity studies on key matrix, fracture system and all other shale related properties were performed and the results were presented. This study attempted to show how sensitivity analysis applied to this model can be used to aid in the design and history matching of a complex shale gas system.
In the second part of this paper the state-of-the-art technology using Artificial Intelligence and Data Mining (AI&DM) techniques which is called single well shale surrogate reservoir model (S3) has been built. Shale surrogate reservoir model is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing shale gas reservoir simulation models. This model was defined as a replica of the shale gas reservoir simulation model that ran and provided accurate results in real-time very fast and can be used for automatic history matching, real-time optimization, real-time decision-making and quantification of uncertainties. The intelligent model was verified using several completely blind simulation runs.
A vibrant and fast-growing literature exists related to various aspects of gas shales, including operational (e.g., drilling, completion, and production) and technological challenges. The latter mainly involves difficulties in formation evaluation/characterization, in modeling gas-matrix-fracture phenomena, and in developing reliable reservoir simulators. In times, these studies directly point to difficulties in accurately predict the ultimate gas recovery and to explain high variability in gas well productivity, which are common to nearly all shale gas reservoirs.
Shale gas plays, which require maximum reservoir exposure to be economic, have been solved through the use of long horizontal wells that are fractured in multiple zones along their several-thousands feet length ; therefore horizontal completions are one of the key things that have led to all of the successes.
|File Size||1 MB||Number of Pages||11|