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.

In Abu Dhabi, the Mishrif Formation is developed in the eastern and western parts conformably above the Shilaif Formation and forms several commercial discoveries. The present study was carried out to understand the development of the Mishrif Formation over a large area in western onshore Abu Dhabi and to identify possible Mishrif sweet spots as future drilling locations. To achieve this objective, seismic mapping of various reflectors below, above, and within the Mishrif Formation was attempted. From drilled wells all the available wireline data and cores were studied. Detailed seismic sequence stratigraphic analysis was carried out to understand the evolution of the Mishrif Formation and places where the good porosity-permeability development and oil accumulation might have happened. The seismic characters of the Mishrif Formation in dry and successful wells were studied and were calibrated with well data. The Mishrif Formation was deposited during Late Cretaceous Cenomanian time. In the study area it has a gross thickness ranging from 532 to 1,269 ft as derived from the drilled wells; the thickness rapidly decreases eastward toward the shelf edge and approaching the Shilaif basin. The Mishrif was divided into three third-order sequences based on core observations from seven wells and log signatures from 25 wells. The bottom-most sequence Mishrif 1.0 was identified is the thickest unit but was also found dry. The next identified sequence Mishrif 2.0 was also dry. The next and the uppermost sequence identified as Mishrif 3.0 shows a thickness from 123 to 328 ft. All the tested oil-bearing intervals lie within this sequence. This sequence was further subdivided into three fourth-order sequences based on log and core signatures; namely, Mishrif 3.1, 3.2, and 3.3. In six selected seismic lines of 181 Line Km (LKM) cutting across the depositional axis, seismic sequence stratigraphic analysis was carried out. In those sections all the visible seismic reflectors were picked using a stratigraphic interpretation software. Reflector groups were made to identify lowstand systems tract, transgressive systems tract, maximum flooding surface, and highstand systems tract by tying with the observations of log and core at the wells and by seismic signature. Wheeler diagrams were generated in all these six sections to understand the lateral disposition of these events and locales of their development. Based on stratigraphic analysis, a zone with likely grainy porous facies development was identified in Mishrif 3.0. Paleotopography at the top of Mishrif was reconstructed to help delineate areas where sea-level fall generated leaching-related sweet spots. Analysis of measured permeability data identified the presence of local permeability baffles affecting the reservoir quality and hydrocarbon accumulation. This study helped to identify several drilling locations based on a generic understanding of the Mishrif Formation. Such stratigraphic techniques can be successfully applied in similar carbonate reservoirs to identify the prospect areas.

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.