fax 01-972-952-9435. Abstract Important hydrocarbon accumulations occur in platform carbonates of the Lower Cretaceous Kharaib (Barremian and Early Aptian) and Shuaiba (Aptian) formations (Upper Thamama Group) of Abu Dhabi. A new, sequence stratigraphy-keyed static (geological) model has been built for the Upper Thamama (Lower Cretaceous) Kharaib and Shuaiba formations. The Kharaib Formation contains two reservoir units (Lower Kharaib Reservoir Unit and Upper Kharaib Reservoir Unit). The overlying Shuaiba Formation is separated from the Kharaib Formation by the Upper Dense Zone (Hawar) and contains two reservoir units (Lower Shuaiba Reservoir Unit and Upper Shuaiba Reservoir Unit) only partly separated by dense intervals.Core and well-log data of a giant onshore oil field in Abu Dhabi, as well as outcrop data from Wadi Rahabah (Ras Al-Khaimah) were used to establish a sequence stratigraphic framework and a lithofacies scheme; applicable to all four reservoir units and the three dense zones.Six third-order composite sequences are composed of twenty-six fourth-order parasequence sets that form the basic building blocks of a new generation static model.On the basis of faunal content, texture, sedimentary structures, and lithologic composition, fourteen reservoir lithofacies and ten non-reservoir (dense) lithofacies are identified from core. Reservoir units range from lower ramp to shoal crest to near back shoal open platform environments.A new static (geological) model has been built to provide a more detailed reservoir description to the dynamic model to further optimize the field development plan.
Reservoir simulation is the industry standard for reservoir management that is used in all phases of field development. As the main source of information, prediction and decision-making, the Full Field Models (FFM)
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petrophysical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching. A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. ifields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions. This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.
TX 75083-3836, U.S.A., fax 01-972-952-9435.
Identifying opportunities in the installed capacity and proactively mitigating the limiting factors are paramount objectives for pursuing profitable production assurance. Although integrated asset modeling has been the de facto technology for supporting production planning and optimization work processes, its application is not fully adopted as it presents challenges when attempted to be used in a large-scale of multiple oil and gas assets. This paper describes ADNOC’s innovative approach to develop a large scale subsurface to surface integrated asset modeling (LSSSIAM) solution by focusing on the desired business outcome. The paper introduces a new concept of right complexity modeling (RCM) to drive the type and level of complexity of the model/simulation based on the desired business outcome and other factors that influence the quality of the decision-making process. The methodology has been applied on a large-scale of multiple assets for effective production assurance that integrates the subsurface to the surface physical phenomena as required by the desired business outcome—the technical assurance of production plans within the context of a country. For the presented example, the proposed methodology resulted in the design of a solution where the subsurface phenomena are represented with a data-driven model to specifically address the requirements of the decision-making process which the solution supports. This resulted in the development of a first-of-its-kind countrywide production model that rigorously considers the properties and physics from the wells to the point of supply while also considering the subsurface phenomena as related to the production potential of the reservoirs and wells. The solution leverages the rigor of first-principle reservoir models to obtain a data-driven proxy model suitable for integration with a first-principle model covering more than 7,000 wells, multiple network and asset facilities, and a supply point transfer countrywide network. The solution can run in a matter of seconds, allowing for the optimization of a desired objective function or the effective analysis of operational scenarios, which can include short- and mid-term production assurance, opportunities identification to increase production to capture value opportunities from a country-wide production capacity context, and compensating for possible shortfalls resulting from unplanned operational disturbances in other assets.
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