History Matching of Production Performance for Highly Faulted, Multi Layered, Clastic Oil Reservoirs using Artificial Intelligence and Data Analytics: A Novel Approach
- Nis Ilyani Mohmad (Petronas Carigali Sdn. Bhd) | Dipak Mandal (Petronas Carigali Sdn. Bhd) | Hadi Amat (Petroliam Nasional Berhad) | Ali Sabzabadi (Petroliam Nasional Berhad) | Rahim Masoudi (Petroliam Nasional Berhad)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil & Gas Conference and Exhibition, 17-19 November, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 7.6.7 Neural Networks, 5.1.2 Faults and Fracture Characterisation, 6 Health, Safety, Security, Environment and Social Responsibility, 7.1 Asset and Portfolio Management, 6.1 HSSE & Social Responsibility Management, 7.6 Information Management and Systems, 7.6.4 Data Mining, 5 Reservoir Desciption & Dynamics, 7.6.6 Artificial Intelligence, 6.1.5 Human Resources, Competence and Training, 5.5 Reservoir Simulation, 7.1.6 Field Development Optimization and Planning, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.1.5 Geologic Modeling, 5.5.8 History Matching, 7.2 Risk Management and Decision-Making, 7 Management and Information
- Reservoir Model, Neural Network, History Matching, Data Analytics
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- 19 since 2007
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History Matching (HM) is one of the critical steps for dynamic reservoir modelling to establish a reliable predictive model. Numerous approaches have emerged over the decades to accomplish a robust history matched reservoir model ranging from the classical reservoir engineering approach to the widely accepted 3D numerical simulation approach and its variations. As geological complexity of the oil and gas field increases (multilayered reservoirs, heavily faulted) compounded with completion complexity (dual strings, commingle production), building a fully representative predictive reservoir model can be arduous to almost impossible task.
Artificial Intelligence (AI) and machine learning has advanced almost all major industries, including the petroleum industry in general and reservoir engineering. The objective of this paper is to outline a novel approach in history matching using a data-driven approach through Artificial Intelligence via Artificial Neural Network (ANN) and Data-Driven Analytics.
In this paper, a step by step methodology in building a reservoir model and history matching process using ANN will be described which includes data preparation and data QA/QC, spatiotemporal database formulation, reservoir model design, ANN architecture design, model training and history matching strategy. A case study of the implementation to Field "A" in Malaysian waters is presented where good to fair history matching quality was obtained for all production parameters. Field "A" is a 25kmx75km oil sandstone reservoirs of a highly geologically complex field (more than 200 major and minor faults, more than 30 reservoir layers) of more than 25 years of production. The challenges of history matching of this field does not only lie on its geologically complex structure and its corresponding subsurface uncertainties, but also on the production strategy of the wells that involved commingled dual strings production with several integrity issues that adds additional dimensions to the field's complexities. To date, Field "A" has no field wide history matched reservoir model using conventional numerical simulation method available due to the complexity of history matching. This long history matching woe is mitigated via the implementation of AI based reservoir model and Data Analytics. This novel approach is estimated to be more time and cost-efficient compared to the conventional method.
The comparison of this AI based reservoir model and history matching methodology with the conventional numerical reservoir model approach will be discussed. Furthermore, the advantages, limitations and areas of improvements of this AI based history matching methodology will also be highlighted.
The target audience of this paper would be to reservoir engineering practitioners and dynamic model simulators who is interested to learn the complementary or alternative approach in reservoir modelling apart from conventional numerical modelling in order to create time-efficient reservoir model and reducing the risks in their field development plans.
|File Size||1 MB||Number of Pages||19|