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Abstract Petroleum Industry has a lot of uncertainties and risks in each element of its different domains starting from exploration reaching to production. Different tools are being used in order to minimize those uncertainties especially in characterizing reservoirs and defining the optimum development strategies for them. One of the most powerful tools is the numerical reservoir simulation which is considered the state of the art technique since 1990's. Numerical reservoir simulation uses certain geological and engineering findings and predicts the reservoir performance in the future stochastically. It helps a lot in maximizing the asset value and hence the project economics. Geological Model or what is called “Static Modeling” is the most important stage in the reservoir modeling process at which we build our model to capture all structural, stratigraphical and petrophysical features. The static model is usually built by the geoscientists then delivered to the engineer to start feeding the model with the dynamic data (e.g. PVT, SCAL, Production, Pressure, VLP… etc.). But before feeding the static model, each reservoir engineer must check the quality of the Static Model to assure certain feature such as; it had been coarsened or upscaled properly, it can really mimic the fluid flow directions, it can be run by numerical solvers available in commercial Simulators without any convergence error, it is valid for the purpose it was built for and much more others. That's why it is very important for each engineer that is willing to work on static models to have a road map of what he/she should do and what type of interactions with geoscientists needed in order to QC the static model before getting it to be Dynamic Model. Indeed, we generalized definite steps which can be considered as a template for any reservoir engineer to follow when he/she approaches to QC any static model. Those steps include all the QC elements starting from Structural aspects like faults modeling reaching to matching with electric logs and facies description. Not only the Static Model QC but also we proposed many recommendations during the Upscaling stage and how to keep your model as simple as you can. All of those were stated clearly combined with some technical details behind each step to let engineers be aware of the technicality in background and its impact. This paper is important for any engineer dealing with reservoir modeling which will provide him with a general scheme to follow whilst QC'ing the model. This paper also targets the methodology to obtain well-defined model in order to have a better realization of our reservoir and hence better FDP and maximum asset value which is the ultimate target for any E&P company in our world.
The Merriam-Webster Dictionary defines simulate as assuming the appearance of without the reality. Simulation of petroleum reservoir performance refers to the construction and operation of a model whose behavior assumes the appearance of actual reservoir behavior. The model itself is either physical (for example, a laboratory sandpack) or mathematical. A mathematical model is a set of equations that, subject to certain assumptions, describes the physical processes active in the reservoir. Although the model itself obviously lacks the reality of the reservoir, the behavior of a valid model simulates--assumes the appearance of--the actual reservoir. The purpose of simulation is estimation of field performance (e.g., oil recovery) under one or more producing schemes. Whereas the field can be produced only once, at considerable expense, a model can be produced or run many times at low expense over a short period of time. Observation of model results that represent different producing ...
Abstract Streamline and streamtube methods have been used in fluid flow computations for many years. Early applications for hydrocarbon reservoir simulation were first reported by Fay and Pratts in the 1950s. Streamline-based flow simulation has made significant advances in the last 15 years. Today's simulators are fully three-dimensional and fully compressible and they account for gravity as well as complex well controls. Most recent advances also allow for compositional and thermal displacements. In this paper, we present a comprehensive review of the evolution and advancement of streamline simulation technology. This paper offers a general overview of most of the material available in the literature on the subject. This work includes the review of more than 200 technical papers and gives a chronological advancement of streamline simulation technology from 1996 to 2011. Firstly, three major areas are identified. These are development of streamline simulators, enhancements to current streamline simulators and applications. In view of the fact that this state of-the-art technology has been employed for a wide range of applications, we defined three major application areas that symbolize the relevance and validity of streamline simulation in addressing reservoir engineering concerns. These are history matching, reservoir management and upscaling, ranking and characterization of fine-grid geological models. Streamline simulation has undergone several phases within its short stretch in the petroleum industry. Initially, the main focus was on the speed advantage and less on fluid flow physics. Next, the focus was shifted to extend its applicability to more complex issues such as compositional and thermal simulations, which require the inclusion of more physics, and potentially reducing the advantage of computational time. Recently, the focus has shifted towards the application of streamline technologies to areas where it can complement finite difference simulation such as revealing important information about drainage areas, flood optimization and improvement of sweep efficiency, quantifying uncertainties, etc.
Lie, K. -A. (SINTEF) | Kedia, K.. (ExxonMobil Upstream Research Company) | Skaflestad, B.. (SINTEF) | Wang, X.. (ExxonMobil Upstream Research Co.) | Yang, Y.. (ExxonMobil Upstream Research Co.) | Wu, X. -H. (ExxonMobil Upstream Research Co.) | Hoda, N.. (ExxonMobil Upstream Research Co.)
Abstract This paper presents a general framework for constructing effective reduced-order models from an existing high-fidelity reservoir model, irrespective of grid topology. We employ a flexible hierarchical grid coarsening strategy that is designed to preserve geologic features and structures in the underlying model such as environments of deposition and faults. The strategy supports selecting and combining coarsening methods that are targeted to the flow patterns in different parts of the reservoir. This includes, but is not limited to, explicit user-imposed boundaries, using efficient field-wide flow indicators, topological and geometric partitioning and methods for amalgamating and splitting clusters of cells. Collectively, these schemes enable an automatic strategy that separates a model into flow-dependent compartments that are respectively close to, far away from, or in between regions of sharp flow transients such as wells. These compartments may then be coarsened using different tailored techniques and target grid resolutions providing much more flexibility compared to traditional coarsening methods. We demonstrate that various techniques for flow-based transmissibility upscaling can be deployed on the resulting coarsened model to compute effective model properties. The hierarchical construction strategy allows efficient exploration of the geologic features of a reservoir that most impact flow patterns and well communication. The coarsened models are shown to be rank and trend accurate, enabling a more exhaustive sensitivity analysis if needed. We study the accuracy of the reduced-order model with a particular emphasis on the upscaled model's ability to capture effects of multiple phases in simulation runs compared to the full high-fidelity model.
Ma, Eddie ((1)Kuwait Oil Company) | Ryzhov, Sergey ((2)Schlumberger) | Gheorghiu, Sorin ((2)Schlumberger) | Hegazy, Osama ((2)Schlumberger) | Banagale, Merlon ((1)Kuwait Oil Company) | Ibrahim, Muhammad ((2)Schlumberger) | Grupinar, Omer ((2)Schlumberger) | Dashti, Laila ((1)Kuwait Oil Company) | Filak, Jean-Michel ((3)Beicip-Franlab) | Al-Houti, Reham ((1)Kuwait Oil Company) | Ali, Farida ((1)Kuwait Oil Company)
Abstract The Greater Burgan field in Kuwait is the largest clastic oil field in the world. Its sheer size, complex geology, intricate surface facility network, over 2,200 well completions and 65-years of production history associated with uncertainty present formidable challenges in reservoir simulation. In the last two decades, many flow simulation models, part-field and full-field, were developed as reservoir management tools to study depletion plan strategies and reservoir recovery options. The new 2011 Burgan reservoir simulation effort was not just another simulation project. Indeed, it was a major undertaking in terms of technical and human resource. The model size, innovative technology, supporting resources, integrated workflows and meticulous planning applied to this project were unprecedented in the history of the Greater Burgan field development. This paper describes work done to prepare a representative numerical model which could be utilized to optimize the remaining life of the reservoir complex. Right from the onset, representative numerical modeling concerns were identified. These led to a systematic collaboration framework being built in place between the static and dynamic modeling teams. Calibration of the model to the historical observations was executed at three levels, Global, Regional and Wells – the Cascade Approach. The cascade approach was designed to enable a concerted model calibration effort in accordance with the recurrent data quality. For instance, while the total field production history attains a high degree of accuracy, the data at the regional Gathering Center (GC) is of a lower level of certainty, but far more reliable than the data at an individual well. Commercial modeling software have been utilized extensively to produce several utilities such as water encroachment maps, Repeat Formation Tester (RFT) matching tools and aquifer definition and adjustment workflows. Subsequently, synergy in the integrated use of these tools produced a robust model calibration process on all three levels in the cascade approach. The main goal of the project – development of a predictive simulation model, always remained at the fore of the project team's mind during the model calibration. Check-point prediction models were defined and constructed at regular intervals during the model calibration phase. This approach allowed qualitative assessment on the evolution towards a representative numerical model. Furthermore, it allowed synchronizing simulation workflows and expedited project deliverables. The overall result was a sound full-field reservoir simulation model that achieved a good match of production, pressure and saturation histories, leading to reliable forecasting of oil recovery under different development scenarios.