Improved Reservoir Description Through History Matching: A New Technique With Application to a Giant Deepwater Oilfield

Abstract The use of computer-aided history matching techniques to assist in the reservoir description process is becoming a standard procedure in the petroleum industry. As computers become faster and more computing power is affordable, practical applications involving history-matching techniques are becoming feasible. This paper describes an automated technique to assist in the history matching process. The developed technique is based on a global optimization method known as stochastic evolution. It is easy to implement, robust with respect to non-optimal solutions and can be easily parallelized. The reservoir parameters are estimated at reservoir scale by solving an inverse problem. At each iteration, a limited number of reservoir parameters are adjusted. Then, a black oil reservoir simulator is used to evaluate the impact of these new parameters on the field production data. Finally, after comparing the simulated production curves to the field data, a decision is made to keep or reject the altered parameters tested. The efficient computer code developed was used to model main reservoir heterogeneities of a giant deepwater reservoir. The reservoir under study is a sandrich turbidite where shale distribution is the most important control on fluid flow. This reservoir has been producing by water injection. The history matching process focused on finding the optimal vertical transmissibility multiplier in order to adjust the average pressure and observed production data. After optimization a good match was obtained for all the dynamic field data (pressure and production of oil and water). The solution achieved to the real case problem enhanced the quality of the reservoir description and provided the reservoir engineers with a better basis for reservoir management. Introduction The main focus of the reservoir characterization and simulation area is the construction of a reservoir model (its external geometry and also internal properties). This model is represented numerically in a 3D collection of data and then serves as the input for a numerical reservoir flow simulator. This numerical tool will solve the flow equations representative of the flow of oil, gas and water inside the reservoir. Its output is the expected performance production curve given a particular production/injection well pattern. The optimization of huge investments allocated to reservoir exploitation strategies fundamentally depends on the precision of this reservoir performance production forecasts. Consequently the knowledge of this reservoir model is one of the key aspects of the overall reservoir management process. The construction of the reservoir model is not a trivial problem. It is an inverse problem. Inverse problems are, mathematically speaking, ill posed problems for which several solutions can be equally achieved. To reduce solution non-uniqueness, the strategy is to integrate as much data as possible when solving the inverse problem. For the reservoir description problem, two broad classes of data have to be considered: the static data (such as core, log, and seismic data) and the dynamic data (such as transient pressures, saturations and flow rates). While most of the static data can be easily integrated during the construction of the model, the integration of dynamic data is not so easy.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.