An Accelerated Adjoint Method for Model Maturation to Update Static Models with Time-Lapse Reservoir Surveillance Data
- Faruk O. Alpak (Shell International Exploration and Production Inc.) | James W. Jennings (Shell International Exploration and Production Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 September - 2 October, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- machine learning, adjoint method, iterative linear solver, model maturation, reservoir simulation
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We develop a novel ensemble model-maturation method that is based on the Randomized Maximum Likelihood (RML) technique and adjoint-based computation of objective function gradients. The new approach is especially relevant for rich data sets with time-lapse information content. The inversion method that solves the model-maturation problem takes advantage of the adjoint-based computation of objective function gradients for a very large number of model parameters at the cost of a forward and a backward (adjoint) simulation. The inversion algorithm calibrates model parameters to arbitrary types of production data including time-lapse reservoir-pressure traces by use of a weighted and regularized objective function. We have also developed a new and effective multigrid preconditioning protocol for accelerated iterative linear solutions of the adjoint-simulation step for models with multiple levels of local grid refinement. The protocol is based on a geometric multigrid (GMG) preconditioning technique. Within the model-maturation workflow, a machine-learning technique is applied to establish links between the mesh-based inversion results (e.g., permeability-multiplier fields) and geologic modeling parameters inside a static model (e.g., object dimensions, etc.). Our workflow integrates the learnings from inversion back into the static model, and thereby, ensures the geologic consistency of the static model while improving the quality of ensuing dynamic model in terms of honoring production and time-lapse data, and reducing forecast uncertainty. This use of machine learning to post-process the model-maturation outcome effectively converts the conventional continuous-parameter history-matching result into a discrete tomographic inversion result constrained to geological rules encoded in training images.
We demonstrate the practical utilization of the adjoint-based model-maturation method on a large time-lapse reservoir-pressure data set using an ensemble of full-field models from a reservoir case study. The model-maturation technique effectively identifies the permeability modification zones that are consistent with alternative geological interpretations and proposes updates to the static model. Upon these updates, the model not only agrees better with the time-lapse reservoir-pressure data but also better honors the tubing-head pressure as well as production logging data. We also provide computational performance indicators that demonstrate the accelerated convergence characteristics of the new iterative linear solver for adjoint equations.
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Chen, C., Gao, G., Li, R., Cao, R., Chen, T., Vink, J.C., and Gelderblom, G. 2018. Global-Search Distributed-Gauss-Newton Optimization Method and Its Integration with the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance. SPE Journal v. 23, no. 5, p. 1496–1517.
Joosten, G.J.P., Altintas, A., and De Sousa, P. 2011. Practical and Operational Use of Assisted History Matching and Model-Based Optimisation in the Salym Field. Paper SPE-146697-MS presented at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., 30 October – 2 November.
Joosten, G.J.P., Altintas, A., van Essen, G., van Doren, J., Gelderblom, P., van den Hoek, P., and Foreste, K. 2014. Reservoir Model Maturation and Assisted History Matching Based on Production and 4D Seismic Data. Paper SPE-170604-MS presented at the SPE Annual Technical Conference and Exhibition held in Amsterdam, The Netherlands, 27-29 October.
Kahrobaei, S., van Essen, G.M., Van Doren, J.F.M., van den Hof, P.M.J., and Jansen, J.D. 2013. Adjoint-Based History Matching of Structural Models Using Production and Time-Lapse Seismic Data. Paper SPE-163586-MS presented at the SPE Reservoir Simulation Symposium held in The Woodlands, Texas, U.S.A., 18-20 February.
van Doren, J., van Essen, G., Wilson, O.B., and Zijlstra, E.B. 2012. A Comprehensive Workflow for Assisted History Matching Applied to a Complex Mature Reservoir. Paper SPE-154383-MS presented at the EAGE Annual Technical Conference & Exhibition incorporating SPE Europec held in Kopenhagen, Denmark, 4-7 June.
van Essen, G., Jimenez, E., Przybysz-Jarnut, J.K., Horesh, L., Douma, S.G., van den Hoek, P., Conn, A., and Mello, U.T. 2012. Adjoint-based History-Matching of Production and Time-lapse Seismic Data. Paper SPE-154375-MS presented at the EAGE Annual Technical Conference & Exhibition incorporating SPE Europec held in Kopenhagen, Denmark, 4-7 June.