Development of appropriate strategy for the management of reservoirs with sanding problems is rather complex and requires an integrated approach to finding the optimum solution to solving the problem. This requires integration of key aspects of reservoir characterisation, drilling, completion and production technologies including sand tolerances (Seabed wellhead/flow lines, topside facilities. Providing an accurate forecast of the tolerance depends on accurate prediction of sand failure and the corresponding volume of produced sand. This is a transient phenomena further complicated by gas reservoir fluid flow. In this paper the results of a comprehensive Thick Wall Cylinder[TWC] experimental sand production studies carried out on synthetic sandstones are presented. The sand production prediction model for liquid flow are further calibrated and upscaled with field data for gas reservoirs. The prediction model developed is further validated with independent field data with good results. The results represent a first for sand production forecast for gas reservoirs. Mitigation of sanding requires reliable sanding prediction, precise well design, accurate technology selection as well as optimum completion strategy.

History matching is widely considered as the most time- and resource-consuming phase of reservoir simulation modeling. Even with the advent of modern, computer-assisted, history matching methods, the dynamic calibration of large-scale simulation models represents a considerable computational undertake. The challenges become even more pronounced with incorporation of subsurface and production uncertainty. This paper outlines a step forward in acceleration of reservoir simulation studies by applying a split/merge approach constrained by no-flow boundary drainage region. The method transforms the history-matching process into an accelerated progressive sequence of dynamic model updates in time and space. Each segment defined as distinctive drainage region, the boundaries of the drainage regions are mapped based on no-flow conditions. Each segment is dynamically calibrated and history matched simultaneously in parallel. Lastly, the segments are merged back to reconstruct the original model to run the prediction phase. The detail of the workflow is described, as well as the implementation of the workflow in a synthetic model. A comparison between the conventional approach and the new approach is discussed. Recommendation and a way forward are shared to capitalize on the accelerated method for future reservoir studies.

Accomplishing an acceptable history match of a reservoir simulation model with historical field performance data is a challenging task. The resources needed for assisted history matching for giant reservoirs is very demanding. Providing engineers with an optimized workflow is essential for these giant simulation models to accomplish the desired history match objectives in a timely manner. Reservoir engineers typically use one of many available commercial packages, some of them are stochastic in nature. These methodologies require users to define regions to guide the optimizer where to apply the modifiers. It is difficult to define a modifier region that can be used to relate the physics and fluids movements in the reservoir while preserving key geological signatures. All these concerns, if addressed properly can contribute to better understanding of the reservoir dynamics, reduce turnaround time of the project, improve quality of the history match, and optimize resources utilization. The work presented in this paper proposes a practical workflow to assist reservoir engineers identify important flow communications regions in the reservoir using streamlines properties. Many Producer-Injector pair regions are identified using streamlines, and an efficient strategy is used to select a handful of regions to apply permeability modifiers, for example. This workflow has been validated by history matching one of the giant fields in Saudi Arabia. A commercial history matching package and a customized streamlines tracing tool was used in this study. The results show improvements in the history match quality and reduction of overall history matching time with the current streamlines-guided workflow when compared to the conventional workflows that use the commercial package alone. The paper shows the impact of streamlines map choices on the quality of history match, turnaround time of history matching workflow, number of required simulation runs to complete a study and other relevant measures.

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The algorithms and workflows have been developed to couple efficient model parameterization with stochastic, global optimization using a Multi-Objective Genetic Algorithm (MOGA) for global history matching, and coupled with an advanced workflow for streamline sensitivity-based inversion for fine-tuning. During parameterization the low-rank subsets of most influencing reservoir parameters are identified and propagated to MOGA to perform the field-level history match. Data misfits between the field historical data and simulation data are calculated with multiple realizations of reservoir models that quantify and capture reservoir uncertainty. Each generation of the optimization algorithms reduces the data misfit relative to the previous iteration. This iterative process continues until a satisfactory field-level history match is reached or there are no further improvements. The fine-tuning process of well-connectivity calibration is then performed with a streamlined sensitivity-based inversion algorithm to locally update the model to reduce well-level mismatch. In this study, an application of the proposed algorithms and workflow is demonstrated for model calibration and history matching. The synthetic reservoir model used in this study is discretized into millions of grid cells with hundreds of producer and injector wells. It is designed to generate several decades of production and injection history to evaluate and demonstrate the workflow. In field-level history matching, reservoir rock properties (e.g., permeability, fault transmissibility, etc.) are parameterized to conduct the global match of pressure and production rates. Grid Connectivity Transform (GCT) was used and assessed to parameterize the reservoir properties. In addition, the convergence rate and history match quality of MOGA was assessed during the field (global) history matching. Also, the effectiveness of the streamline-based inversion was evaluated by quantifying the additional improvement in history matching quality per well. The developed parametrization and optimization algorithms and workflows revealed the unique features of each of the algorithms for model calibration and history matching. This integrated workflow has successfully defined and carried uncertainty throughout the history matching process. Following the successful field-level history match, the well-level history matching was conducted using streamline sensitivity-based inversion, which further improved the history match quality and conditioned the model to historical production and injection data. In general, the workflow results in enhanced history match quality in a shorter turnaround time. The geological realism of the model is retained for robust prediction and development planning.