Full-Field Parallel Simulation Model: A Unique Tool for Reservoir Management of the Greater Burgan Oil Field
Abstract: The Greater Burgan field in Kuwait is the largest clastic oil reservoir in the world. Reservoir simulation in this gigantic reservoir presents formidable challenges in any modeling effort. Its sheer size, complex geology, intricate surface facility network, 2,200 completions, and 58-years of production history with significant uncertainty represented a daunting task in history-match and prediction. The quest began in 2002 with the creation of a 65-million cells' geostatistical earth model. Th… Show more
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Cited by 13 publications
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Summary Determination of the operating conditions of a field under a set of physical system constraints (e.g., compressor limits) and engineering preferences (e.g., voidage replacement) is a primary concern for petroleum engineers. Rule-based systems have been proposed for this, but the process is most suitably defined as an optimization problem. An optimization procedure that uses mixed-integer linear programming (MILP) is discussed in this study. Well rates that honor system and engineering constraints are handled simultaneously while the maximum for an objective is calculated (e.g., field oil rate or cash revenue). Optimal rates for the current conditions of the field are determined. Note that this results in instantaneous optimization and, thus, cannot account for recurrent events such as water breakthrough. Nevertheless, an efficient and robust instantaneous optimizer is useful within a grander optimization scheme, short forecast periods and, also, in real-time allocation situations. The approach is able to efficiently handle the nonlinearities in the system by way of piecewise linear functions. Also, as a result of the formulation, the exact optimal solution of the problem is guaranteed. Another property of the approach is that, in cases in which it is not possible to honor all the targets and limits of the system simultaneously, a scheme is introduced that enables the engineer to prioritize the constraints. This prioritization scheme proves to be of great practical significance because most real cases have conflicting targets and limits that result in optimization systems with no feasible solutions. Also, a heuristic is used that ensures realistic results by elimination of mathematical artifacts (rate oscillations in time) that often arise when the reservoir contains wells with similar properties [e.g., water/oil ratio (WOR) and gas/oil ratio (GOR)]. The optimization system is applied to synthetic cases and two real-field cases. The real-field cases pose problems that cannot be handled by conventional rule-based systems. Introduction Production and injection allocation with the objective of maximizing profits while simultaneously honoring all facilities limits, contractual targets, and engineering preferences, is a process that can best be addressed as an optimization problem. Optimization techniques have been applied to a variety of oil-field-development problems. Early approaches used linear programming (LP) techniques to solve rate-allocation problems with linear constraints, a linear objective, and continuous parameters (Brown et al. 1998; Bohannon 1970; Lang and Horne 1983; Lo et al. 1995). MILP methods, however, enable optimization on discrete and continuous variables in which discrete variables are commonly used to model decisions or approximate nonlinear functions. For example, Saif et al. (1987) were interested in determining production allocation when considering several reservoirs and used discrete variables to model selection of a production profile from a set of predefined profiles for each reservoir. The profiles were generated by use of aggregate production from all wells in the reservoir, which simplied well performance and interaction. Fang and Lo (1996) demonstrated how separable programming techniques can be used to optimize oil production during artificial lift with gas injection; for each well, flow performance vs. amount of lift gas was represented as a piecewise linear curve, and the optimizer determined the best allocation of gas lift under various facility constraints. For wells not under gas lift, the wellbore performance was represented with a simple linear relationship between the phase flowing rates that assumed equal scaling of phase flow rates. This approach is similar to that presented by Wang et al. (2002a) in which piecewise linear curves were used to model the well performance for all wells, not just those on gas lift. Nonlinear optimization methods, such as sequential quadratic programming, have also been used to optimize coupled reservoir-facility models in systems in which the gathering system has significant impact on individual well performance (Wang et al. 2002b; Davidson and Beckner 2003). These methods are capable of incorporating pipe and facility devices in the formulation and capturing gathering system impact of individual well performance. However, they lose the ability to incorporate discrete variables representing common well-management actions such as producing at a minimum flow rate or shutting in the well. Optimization is still not widely used and accepted as a best-practice approach for rate allocation in the reservoir-engineering community. Sequential rule-based heuristic systems are still the most common form of well-management systems (Wijesinghe et al. 1983). Apart from the engineer's familiarity with rule-based systems, one reason for their persistence might be that, although these systems often deliver suboptimal results, they are robust. The systems hardly fail in delivering a solution no matter how suboptimal it may be. Here, we present a similarly robust and easy-to-use allocation-optimization framework that is advantageous when compared to rule-based systems in several ways:Ability to define an objective to be maximized: This also brings in the ability to penalize the production of certain phases or components.Ability to handle all operating constraints simultaneously: Rule-based systems often deliver suboptimal solutions because they fail to capture the dependencies among the operating constraints as a result of their sequential nature.Ease of use: Removes the complexity of finding the correct allocation scheme, which, in rule-based systems, might involve defining convoluted logic with dependence among the allocation steps and iterations. The approach presented here focuses on optimization of allocation at a point in time rather than over time. The allocation is conducted in real time in the sense that the future behavior is not taken into consideration while allocating the rates for the current time. While this is a limitation, a robust instantaneous optimization framework works in many situations:Replace sequential rule-based allocation logic to deliver optimal allocation: The use of rule-based allocation systems is currently the common practice in the reservoir-engineering community.Use with a reactive operating strategy. This might be the case when we have confidence in our numerical models to represent the current or near-future situation in the reservoir but we may not have the same level of confidence in the models predictions. For instance, we might not want to proactively shut wells before we observe any water breakthrough despite the model's predictions indicating this would be beneficial over time.Use for real-time production optimization. This point is related to the previous one.Use as the inner loop in grander optimization schemes. As compared to previous allocation-optimization approaches, (Wang et al. 2002a; Davidson et al. 2003) we present a prioritization scheme that enables the prioritization of the operating conditions to deliver the desired allocation in the case of conflicting targets and limits. Additionally, techniques are developed that avoid unrealistic operating scenarios that result from continually solving for the optimal solution.
Summary Determination of the operating conditions of a field under a set of physical system constraints (e.g., compressor limits) and engineering preferences (e.g., voidage replacement) is a primary concern for petroleum engineers. Rule-based systems have been proposed for this, but the process is most suitably defined as an optimization problem. An optimization procedure that uses mixed-integer linear programming (MILP) is discussed in this study. Well rates that honor system and engineering constraints are handled simultaneously while the maximum for an objective is calculated (e.g., field oil rate or cash revenue). Optimal rates for the current conditions of the field are determined. Note that this results in instantaneous optimization and, thus, cannot account for recurrent events such as water breakthrough. Nevertheless, an efficient and robust instantaneous optimizer is useful within a grander optimization scheme, short forecast periods and, also, in real-time allocation situations. The approach is able to efficiently handle the nonlinearities in the system by way of piecewise linear functions. Also, as a result of the formulation, the exact optimal solution of the problem is guaranteed. Another property of the approach is that, in cases in which it is not possible to honor all the targets and limits of the system simultaneously, a scheme is introduced that enables the engineer to prioritize the constraints. This prioritization scheme proves to be of great practical significance because most real cases have conflicting targets and limits that result in optimization systems with no feasible solutions. Also, a heuristic is used that ensures realistic results by elimination of mathematical artifacts (rate oscillations in time) that often arise when the reservoir contains wells with similar properties [e.g., water/oil ratio (WOR) and gas/oil ratio (GOR)]. The optimization system is applied to synthetic cases and two real-field cases. The real-field cases pose problems that cannot be handled by conventional rule-based systems. Introduction Production and injection allocation with the objective of maximizing profits while simultaneously honoring all facilities limits, contractual targets, and engineering preferences, is a process that can best be addressed as an optimization problem. Optimization techniques have been applied to a variety of oil-field-development problems. Early approaches used linear programming (LP) techniques to solve rate-allocation problems with linear constraints, a linear objective, and continuous parameters (Brown et al. 1998; Bohannon 1970; Lang and Horne 1983; Lo et al. 1995). MILP methods, however, enable optimization on discrete and continuous variables in which discrete variables are commonly used to model decisions or approximate nonlinear functions. For example, Saif et al. (1987) were interested in determining production allocation when considering several reservoirs and used discrete variables to model selection of a production profile from a set of predefined profiles for each reservoir. The profiles were generated by use of aggregate production from all wells in the reservoir, which simplied well performance and interaction. Fang and Lo (1996) demonstrated how separable programming techniques can be used to optimize oil production during artificial lift with gas injection; for each well, flow performance vs. amount of lift gas was represented as a piecewise linear curve, and the optimizer determined the best allocation of gas lift under various facility constraints. For wells not under gas lift, the wellbore performance was represented with a simple linear relationship between the phase flowing rates that assumed equal scaling of phase flow rates. This approach is similar to that presented by Wang et al. (2002a) in which piecewise linear curves were used to model the well performance for all wells, not just those on gas lift. Nonlinear optimization methods, such as sequential quadratic programming, have also been used to optimize coupled reservoir-facility models in systems in which the gathering system has significant impact on individual well performance (Wang et al. 2002b; Davidson and Beckner 2003). These methods are capable of incorporating pipe and facility devices in the formulation and capturing gathering system impact of individual well performance. However, they lose the ability to incorporate discrete variables representing common well-management actions such as producing at a minimum flow rate or shutting in the well. Optimization is still not widely used and accepted as a best-practice approach for rate allocation in the reservoir-engineering community. Sequential rule-based heuristic systems are still the most common form of well-management systems (Wijesinghe et al. 1983). Apart from the engineer's familiarity with rule-based systems, one reason for their persistence might be that, although these systems often deliver suboptimal results, they are robust. The systems hardly fail in delivering a solution no matter how suboptimal it may be. Here, we present a similarly robust and easy-to-use allocation-optimization framework that is advantageous when compared to rule-based systems in several ways:Ability to define an objective to be maximized: This also brings in the ability to penalize the production of certain phases or components.Ability to handle all operating constraints simultaneously: Rule-based systems often deliver suboptimal solutions because they fail to capture the dependencies among the operating constraints as a result of their sequential nature.Ease of use: Removes the complexity of finding the correct allocation scheme, which, in rule-based systems, might involve defining convoluted logic with dependence among the allocation steps and iterations. The approach presented here focuses on optimization of allocation at a point in time rather than over time. The allocation is conducted in real time in the sense that the future behavior is not taken into consideration while allocating the rates for the current time. While this is a limitation, a robust instantaneous optimization framework works in many situations:Replace sequential rule-based allocation logic to deliver optimal allocation: The use of rule-based allocation systems is currently the common practice in the reservoir-engineering community.Use with a reactive operating strategy. This might be the case when we have confidence in our numerical models to represent the current or near-future situation in the reservoir but we may not have the same level of confidence in the models predictions. For instance, we might not want to proactively shut wells before we observe any water breakthrough despite the model's predictions indicating this would be beneficial over time.Use for real-time production optimization. This point is related to the previous one.Use as the inner loop in grander optimization schemes. As compared to previous allocation-optimization approaches, (Wang et al. 2002a; Davidson et al. 2003) we present a prioritization scheme that enables the prioritization of the operating conditions to deliver the desired allocation in the case of conflicting targets and limits. Additionally, techniques are developed that avoid unrealistic operating scenarios that result from continually solving for the optimal solution.
The Wara reservoir is one of the four main reservoirs in the Greater Burgan field, the world's largest sandstone oil field. It has experienced significant pressure decline after 60 years of primary production. In 2005, design for a pressure maintenance project (PMP) via a peripheral waterflood was initiated to arrest pressure decline and improve oil recovery. A key building block of the Wara PMP is a stand-alone, full-field Wara simulation model. The 23-million cells geological model was scaled-up to 4 million cells for flow simulation. Four pseudo layers were added to the simulation model to allow fluid migration via faults from the lower reservoirs. The new model has 100 m x 100 m areal cells and individual layers with an average thickness of 6 ft. Overall, this new model has 18 times refinement compared to the previous model for the Wara reservoir. Thus, this model is suitable for evaluating PMP, infill drilling and pattern waterflood. This paper, however, focuses on PMP evaluation only carried out over the last four years. The final history-match has been carried out at three levels: Field, Gathering Centers (GC) and Key Wells. Detailed study of interactions among field permeability distribution, edge aquifer representation, and fault transmissibility specifications on simulation results was key in developing a meaningful history-match. PNC data for many wells around the periphery of the field provided useful insights for edge aquifer representation. Water cut match was less than satisfactory for wells located in the center due to modeling deficiency of pseudo layers as discussed in the body of the paper. Prediction runs have been set up to investigate various PMP designs. These runs include sensitivity with respect to number of injectors, number of producers, target injection rate per well, maximum bottom-hole injection pressure, voidage replacement ratio, injector-producer distances, and injector-producer rows along with various scenarios for dealing with production from existing Wara producers throughout the field. This flow simulation model will be used as an operating model to optimize process design and well location. Introduction Greater Burgan, which is located in southeastern Kuwait, covers a surface area of about 320 square miles and has been ranked as the largest clastic oil field in the world. The four main reservoir units comprising the Greater Burgan Field complex are the Wara, Mauddud, Burgan Third Sand and Burgan Fourth Sand. The Greater Burgan Field is separated into three producing areas, Burgan, Magwa and Ahmadi. No structural, geologic or reservoir features distinguish these areas, although PVT differences are assigned for areas north and south of the Graben fault. The Wara and Mauddud reservoirs are separated vertically from the remaining reservoirs by extensive carbonate and shale intervals. However, extensive faulting does allow communication between the Wara sand and the Burgan Sands. Wara reservoir has an average thickness of 160 ft and historically, 336 wells have produced from the Wara reservoir at one time or another.
The Mauddud reservoir in the Greater Burgan field is a thin, carbonate reservoir containing light oil in a 10 to 20-feet (ft) target zone with "good" porosity. Matrix permeability is low, and natural fracture density can be variable in this reservoir. Thus, this reservoir must be exploited using horizontal wells. In the early 1990s, 16 horizontal wells were drilled in this reservoir. Five more horizontal wells have been drilled in 2005 and 2006 in an effort to scope out the long-term potential of this reservoir. However, only three of these five new wells had a production history of a few months that could be used in our history-matching effort. Thus, the history-matching effort concentrated on 19 wells (16 old plus 3 new wells).In conjunction with the drilling of recent horizontal wells, a comprehensive reservoir characterization program culminating into a full-field reservoir simulation model has been completed. The 24-million cell geological model was scaled up to a 9-million cell model at a 164-ft by 164-ft areal grid level to properly incorporate flow characteristics of horizontal wells into the simulation model. Matrix permeability of the scaled-up model was enhanced by using a unique process based on analytical solutions for short fractures and fracture density/orientation mapping for the entire field. This reservoir simulation model has been historymatched for the 13-year production history of 16 1990s horizontal wells along with a production history of a few months for 3 new wells using only a global-permeability multiplier and water relativepermeability curve shape modification. This model has been used in the forecast mode to assess long-term field development opportunity for the Mauddud reservoir. Primary depletion results show that horizontal wells drilled in an intelligent manner in this difficult reservoir hold the key to economic development of this reservoir.
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