Computing hardware and reservoir simulation technologies continue to evolve in order to meet the ever-increasing requirement for improving computational performance and efficiency in the oil and gas industry. These improvements have enabled the simulation of larger and more complex reservoir models. When working with steam assisted gravity drainage (SAGD) operations, determining the optimal steam injection rates and allocation of steam among various multi-well pads is very important, especially given the high cost of steam generation and the current low oil price environment. As SAGD operations mature, steam chambers start to coalesce and interact with each other, forcing producers to face declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining wells to maintain a low SOR and free up capacity for newer, more productive wells. Steam injection and allocation between wells and multiple pads then becomes an exercise of optimizing cost, and improving productivity and net present value (NPV). A case study is performed on a full field SAGD model by optimizing steam delivery aided by Artificial Intelligence (AI) and machine learning enabled algorithms for automated numerical tuning, and dynamic gridding technologies. The model contains 15 pads, 96 well pairs (192 wells), 12.6 million active simulation grid blocks, and represents a typical Athabasca formation geology and fluid properties. The proposed steam delivery optimization considers two main scenarios. The first scenario considers the case in which steam generation capacity is limited, and the optimization process intelligently determines the optimal well and pad level steam injection rates dynamically during the life of the project. The second scenario assumes that steam generation availability is not constrained and the field development plan is optimized based on steam required for maximum recovery from the field as fast as possible. A full field optimized development plan is created for the 15 SAGD pads and 96 well pairs. Following the optimization, an increase in NPV and reduction in SOR is achieved for the entire field due to the efficient utilization of total available steam. The optimization study required several full field SAGD simulations to be completed in a practical time period, demonstrating that workflows such as this can be carried out for full field thermal models. These models can also be used to evaluate production responses due to varying operating strategies in the field. This paper presents the optimization of steam allocation for a full field, multi pad SAGD simulation model. It demonstrates that advances in computing and reservoir simulation technology have enabled the simulation of full field models within a reasonable timeframe, allowing engineers to tackle a new class of problems that were previously impractical.

Modern assisted history matching tools allow an engineer to specify the uncertain parameters in a simulation dataset then perform an optimization to minimize the difference between observed field data and the corresponding simulation outputs. During this optimization, multiple simulations are run, with the uncertain parameters varying within the ranges specified by the engineer. The difference between the observed field data and the simulation output is measured by an objective function. Standard objective functions have been reported in the literature for the difference between observed and simulated production and injection rates, and for measurements in time and space done at observation wells. In this work we incorporate an additional objective function term that measures the difference between the observed and simulated steam chamber location and shape. In addition, the differences in 3D volumes were visualized, which lead to a better physical understanding of what parameters should be adjusted during a history match. For a steam injection process like Steam Assisted Gravity Drainage (SAGD), 4D seismic may be used to determine where a steam chamber is located in a reservoir at a point in time. The objective function developed in this work measures the difference between an observed chamber's shape and location in the reservoir and the corresponding shape and location for the chamber indicated in the simulation output. The objective function is a binary mismatch function, checking each simulation grid block to see if the seismic chamber and the simulation chamber agree or disagree, and calculating the ratio of the total volume of disagreements over the total volume of agreements. This steam chamber mismatch function was included in an assisted history match performed on a well pair from Suncor Energy's SAGD project at Firebag. The inclusion of this additional information added additional constraints to the simulation model, leading to a conceptually more dependable history match and a better geological and dynamic characterization of the reservoir.

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Summary Every simulation engineer wishes to simulate large full-field models, but historically reservoir simulation of the steam-assisted-gravity- drainage (SAGD) process has been constrained to single-well models up to a single pad. Models of these sizes provide valuable information and have helped to assess the development potential of reservoirs. These models may be used for reservoir management and to support the decision-making process for the design of the initial completion, operating strategy, and multipad wind-down evaluations, and also qualitatively assess the uncertainty in the SAGD forecast under different geological settings. However, in many cases we are left with the question of how multiwell and multipad communications ultimately affect performance at the well-pair scale. Because of technological constraints with computer hardware and simulation technology, running extremely large multipad models has been until recently largely impractical, especially when trying to run multiple scenarios to better understand the impact of geological and operational uncertainty. In this paper, we present a new and practical workflow that makes running extremely large multipad, multimillion-grid-cell SAGD models a reality. The three major steps of the workflow are (1) generating simulation-friendly geomodels, (2) use of experimental design and 3D submodels on the basis of SAGD performance index (SPI) for numerical tuning, and (3) use of 2D cross sections and SPI to develop dynamic grid-refinement-parameter values for the full 3D model. All of these steps are intended to improve the numerical stability and run time of multipad SAGD simulation models. A 24-SAGD-well-pair model with 2.52 million gridblocks was simulated for 10 years of forecast. The reservoir is geologically complex and highly heterogeneous. We discuss some of the important aspects that need to be accounted for when simulating large-scale SAGD models. Using this new workflow, the simulation run time was reduced from 42 days to 7 days on eight central processing units (CPUs)—a six-time speedup. The resulting run time is short enough to facilitate multirealization simultaneous runs using eight CPUs, hence maximizing the throughput and minimizing the simulation cycle time. This new workflow can be easily replicated and, more importantly, automated to reduce engineering time requirements. While this paper focuses on the SAGD process, this methodology is completely generic in that it can be applied to any large data set for any process. Details will differ depending on the process, but the workflow will be the same.

fax 01-972-952-9435. AbstractAs development activities in heavy oil and in situ bitumen deposits have accelerated, the challenge of forecasting the performance of in situ recovery processes at field scale has increased exponentially.Delineation drilling results make it apparent that these deposits are highly complex and three-dimensionally heterogeneous. Heterogeneity has a significant impact on the effectiveness and economics of the recovery process.Many experienced operators are recognizing that in addition to the static complexity of the reservoirs it is necessary to consider the dynamic stress state in the regions undergoing production. Geomechanical factors are significant and must be built into any realistic numerical simulation of recovery processes.It has become apparent to operators that modeling single wellpair operations may be misleading, and seven to ten well-pair models are now quite common.All these factors result in increasing size and complexity of numerical simulation models.Reservoir simulator developers have responded with two technologies to achieve reasonable run times in these large and complex models. The combined use of 64-bit symmetrical multiprocessor computers and dynamic grid refinement will be discussed and compared against traditional simulation methods. This paper will provide examples of the application of these leading edge technologies for in situ oil sands development in the Surmont area of the Athabasca deposit.