Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.
Achieving a high-quality history match is critical to understand reservoir uncertainties and perform reliable field-development planning. Classical approaches require large uncertainty studies to be conducted with reservoir-simulation models, and optimization techniques would be applied to reach a configuration where a minimum error is achieved for the history match. Such techniques are computationally heavy, because all reservoir simulations are run in both uncertainty studies and optimization processes. To reduce the computing requirements during the optimization process, we propose to create a robust deep-learning model based on the hidden relationships between the uncertainty parameters and the reservoir-simulation results that can operate as a surrogate model for computationally intensive reservoir-simulation models. In this paper, we present a workflow that combines a deep-learning, machine-learning (ML) model with an optimizer to automate the history-matching process. Initially, the reservoir simulator is run to generate an ensemble of realizations to provide a comprehensive set of data relating the history-matching uncertainty parameters and the associated reservoir-simulation results. This data is used to train a deep-learning model to predict reservoir-simulation results for all wells and relevant properties for history matching from a set of the selected history-matching uncertainty parameters. This deep-learning model is used as a proxy to replace the reservoir-simulation model and to reduce the computational overhead caused by running the reservoir simulator. The optimization solution embeds the trained ML model and aims to deliver a set of uncertainty parameters that minimizes the mismatch between simulation results and historical data. At each optimization iteration, the ML model is used to predict the well-level reservoir-simulation results. At the end of the optimization process, the optimal parameters suggested by the optimizer are then validated by running the reservoir simulator. The proposed work achieves high-quality results by leveraging advanced artificial-intelligence techniques, thus automating and significantly accelerating the history-matching process. The use of uncertainty parameters as input to the deep-learning model, and the model's ability to predict production/injection/pressure profiles for all wells is a unique methodology. Furthermore, the combination of the deep-learning surrogate reservoir model with optimization methods to resolve history-matching problems is advancing the industry's practices on the subject.
This paper presents an integrated subsurface study that focuses on delivering field development planning of two reservoirs via comprehensive reservoir characterization workflows. The upper gas reservoir and lower oil reservoir are in communication across a major fault in the crest area of the structure. Gas from the upper reservoir, which is not under development, is being produced along with some oil producers from the oil reservoir as per acquired surveillance data. Pressure depletion is observed in observer wells of the upper reservoir, which substantiate both reservoirs communication. The oil reservoir is on production since 1994, under miscible hydrocarbon water alternating gas injection (HCWAG) and carbon dioxide (CO2) injection. The currently implemented development plan has been facing several complexities and challenges including, but not limited to, maintaining miscibility conditions, sustainability of production and injection in view of reservoirs communication, reservoir modeling challenges, suitability of monitoring strategy, associated operating costs and expansion of field development in newly appraised areas. In this study, an assessment of multiple alternative field development scenarios was conducted; with an aim to tackle field management and reservoir challenges. It commenced by a comprehensive synthesis of seismic, petrophysical (including extensive core characterizations), geological, production and reservoir engineering data to ensure data adequacy and effectiveness for development planning. The process was followed by evaluation of the historical reservoir management, HCWAG and CO2 injection practices using advanced analytics to identify areas for improvement and accelerate decision making process. The identified areas of improvement were incorporated into a dynamic model via diverse set of field management logics to screen wide range of scenarios. In the final step, the optimal scenarios were selected, in line of having strong economic indicators, honoring operational constraints, corporate business plan and strategic objectives. The comprehensive and flexible field management logic was set up to target different challenges and was used to extensively screen hundreds of different field development scenarios varying several parameters. Examples of such parameters are WAG ratio, injection pressures for both water/gas and CO2, cycle duration, well placement, reservoir production and injection guidelines, different co-development production schemes coupled with static and dynamic uncertainty properties against incremental oil production and discounted cash flow. The simulation results were analyzed using standardized approach where a number of key indicators was cross-referenced to produce optimal field development scenarios with regards to co-development effect of both reservoirs, miscibility conditions, balanced pressure depletion, harmonized sweep as well as robust discounted cash flow. Strong management support, multi-disciplinary data integration, agility of decision making and revisions in a controlled timeframe are considered as the key pillars for success of this study. The adopted workflow covers subsurface modeling aspects from A-Z and following reservoir characterization and modeling best practices. The methodology applied in this study uses an integrated subsurface structured approach to tackle reservoirs challenges and co-development, generate alternative development options leveraging on data analytics techniques and advanced field management strategies.
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