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Collaborating Authors
Results
Abstract Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, Voidage Replacement Ratio (VRR) and Net Present Value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field development activities on the environment while NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well placement optimization has been done mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of one (or close to one) during waterflooding. A multiobjective approach incorporating NPV and VRR is proposed for solving the well placement optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase has the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights were used in the third phase to describe the relative importance of the NPV and the VRR and a comparison of how these weights affect the optimized NPV and VRR values is provided. We applied the method to determine the optimum placement of wells using two sample reservoirs: one with a distributed permeability field and the other, a channel reservoir with four facies. Two evolutionary-type algorithms: the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE), were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary oil recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of one.
- Asia > Middle East (0.46)
- North America > United States (0.46)
- Europe > Austria (0.28)
Abstract Restricted flow capacity of low permeability oil formations imposes unique challenges to the implementation of CO2-WAG processes in such reservoirs. Application of multi-stage fractured horizontal wells can substantially improve the injection and production rates. However, there are various design parameters and operating conditions which can affect the performance of a WAG flood. The parameters considered in this study are those related to development pattern (well spacing and well completion strategy), hydraulic fracture geometry (half-length and spacing), WAG parameters (WAG ratio and CO2 slug size) and the timing of the switch from primary or water-flood to WAG scheme. In this study, CO2 EOR performance is assessed based on the oil recovery factor and also the amount of stored CO2; in other words, the objective is to achieve both the goals of enhanced oil recovery and sequestration of CO2 in the tight oil formation. However, to reflect the effect of time, the net present value (NPV) of the projects was also considered. All three of these parameters were therefore included in objective functions to be optimized. The effect of all aforementioned parameters on objective functions was investigated using a compositional simulator. Design of experiment (DOE) was then utilized to perform a comprehensive statistical analysis to recognize the most prominent factors in fulfillment of each objective function in a tight reservoir with properties similar to Pembina Cardium field. Response surfaces were generated to quantify the effect of the factors on the objective functions. Optimization was carried out to find those sets of factors which provided the highest recovery, storage, and NPV. Searching for optimal values can be extended to any combination of objective functions which are obtained by applying weighting multipliers to each individual objective function.
- North America > Canada > Alberta (0.49)
- North America > United States > Texas > Dallas County (0.28)
Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Russian Oil & Gas Exploration & Production Technical Conference and Exhibition held in Moscow, Russia, 16-18 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited.
- Asia (0.48)
- Europe > Russia > Central Federal District > Moscow Oblast > Moscow (0.24)
Abstract Despite the perception of lucrative earnings in the oil industry, various authors have noted that industry performance is routinely below expectations. For example, Brashear et al. (2001) noted that average return was around 7% in the 1990s, despite using typical project hurdle rates of at least 15%. The underperformance is generally attributed to poor project evaluation and selection due to chronic bias. While a number of authors have investigated cognitive biases in oil and gas project evaluation, there have been few quantitative studies of the impact of biases on economic performance. We believe that incomplete investigation and possible underestimation of the impact of biases in project evaluation and selection are at least partially responsible for persistence of these biases. The objectives of our work were to determine quantitatively the value of assessing uncertainty or, alternatively, the cost of underestimating uncertainty. In this paper we present a new framework for assessing the monetary impact of overconfidence bias and directional bias (i.e., optimism or pessimism) on portfolio performance. For moderate amounts of overconfidence and optimism, expected disappointment was 30-35% of estimated NPV for the industry portfolios and optimization cases we analyzed. Greater degrees of overconfidence and optimism resulted in expected disappointments approaching 100% of estimated NPV. Comparison of modeling results with industry performance in the 1990s indicates that these greater degrees of overconfidence and optimism have been experienced in the industry. The value of reliably quantifying uncertainty is reducing or eliminating expected disappointment (having realized NPV substantially less than estimated NPV) and expected decision error (selecting the wrong projects). Expected disappointment and decision error can be reduced by focusing primarily on elimination of overconfidence; other biases are taken care of in the process. Elimination of expected disappointment will improve industry performance overall to the extent that superior projects are available and better quantification of uncertainty allows identification of these superior projects.
Results of the First Norne Field Case on History Matching and Recovery Optimization Using Production and 4D Seismic Data
Rwechungura, Richard (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway) | Bhark, Eric (3Texas A&M University, College Station, Texas USA) | Miljeteig, Ola T. (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway) | Suman, Amit (4Stanford University, USA) | Kourounis, Drosos (4Stanford University, USA) | Foss, Bjarne (2Department of Engineering Cybernetics, IO center, NTNU) | Hoier, Lars (5Statoil in Trondheim) | Kleppe, Jon (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway)
Abstract In preparation for the SPE Applied Technology Workshop, "Use of 4D seismic and production data for history matching and optimization โ application to Norne (Norway)" held in Trondheim 14-16 June 2011, a unique test case (Norne E-segment) study based on real field data of a brown field offshore Norway was organized to evaluate and compare mathematical methods for history matching as well as methods on optimal production strategy and/or enhanced oil recovery. The integrated data set provided an opportunity to discuss emerging and classical history matching and optimization methods after being tested using real field data. The participants of this comparative case study were expected to come up with a history matched model preferably using an integration of production and time-lapse seismic data and with an optimal production strategy for the remaining recoverable resources for the future period. Participants were allowed to suggest techniques to enhance recovery. Taking into account that the Norne benchmark case is a case study based on real data and no one exactly knows the true answer, participants and delegates were encouraged to discuss the methods, results and challenges during the course of the workshop, and thus in this case there are no winners or losers. Everyone who participated gained experience during the course of the exercise. Participants were asked to history match the model until the end of 2004 and optimally predict the production (oil, water and gas rates) performance until the end of 2008. Participants were from different universities in collaboration with other research organizations namely Stanford University in collaboration with IBM and Chevron, TU Delft in collaboration with TNO, Texas A&M University, and NTNU in collaboration with Sintef. This paper summarizes the presented results from these groups and the outcome of the discussion of the workshop delegates.
- North America > United States > Texas (1.00)
- Europe > Netherlands > South Holland > Delft (0.24)
- Europe > Norway > Trรธndelag > Trondheim (0.24)
- Geology > Geological Subdiscipline > Geomechanics (0.93)
- Geology > Rock Type > Sedimentary Rock (0.67)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Not Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Ile Formation (0.99)
- (8 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- (2 more...)
Summary In this paper, we develop an efficient algorithm for production optimization under linear and nonlinear constraints and an uncertain reservoir description. The linear and nonlinear constraints are incorporated into the objective function using the augmented Lagrangian method, and the bound constraints are enforced using a gradient-projection trust-region method. Robust long-term optimization maximizes the expected life-cycle net present value (NPV) over a set of geological models, which represent the uncertainty in reservoir description. Because the life-cycle optimal controls may be in conflict with the operator's objective of maximizing short-time production, the method is adapted to maximize the expectation of short-term NPV over the next 1 or 2 years subject to the constraint that the life-cycle NPV will not be substantially decreased. The technique is applied to synthetic reservoir problems to demonstrate its efficiency and robustness. Experiments show that the field cannot always achieve the optimal NPV using the optimal well controls obtained on the basis of a single but uncertain reservoir model, whereas the application of robust optimization reduces this risk significantly. Experimental results also show that robust sequential optimization on each short-term period is not able to achieve an expected life-cycle NPV as high as that obtained with robust long-term optimization.
- North America > United States > Texas (0.67)
- North America > United States > Oklahoma (0.46)
Summary Normally only approximately 30% of the oil in a reservoir is extracted during primary production, but using secondary-production methods such as water or gas injection, it is often possible to increase that percentage significantly and maintain the production rate of a reservoir over a longer period of time. In reservoirs under water or gas injection, additional gains can be obtained through an efficient strategy for management of front movement and reservoir sweep. The objective of reservoir production optimization is to maximize an outcome such as sweep efficiency or net present value (NPV) through the control of completion rates or pressures. Using optimization methods, it is possible to compute control settings that result in increased oil production and decreased water production compared with production from standard practices. In this paper, we focus on optimization using sequential quadratic programming (SQP) with an ensemble-based approach to estimate the gradient for the optimization. Although uncertainty in reservoir properties is usually important for the computation of optimal controls, here we use a single realization of the reservoir to evaluate the efficiency of the optimization algorithm. The most expensive aspect of gradient-based optimization is usually the computation of gradients. Most practical production-optimization problems involve large-scale, highly complex reservoir models with thousands of constraints, which makes numerical calculation of the gradient time consuming. Here, we use an ensemble-based approach for finding gradients and use localization to improve estimation of the gradient from a small number of realizations. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for maximizing the objective function, with the Hessian estimated from a sequence of estimates of the gradient. Improving the gradient approximation using localization results in improvement to the Hessian approximation. A second important aspect of the efficiency of the method is the identification of active constraints. In this paper, we use a method for eliminating nonnegativity constraints to decrease computation time and an updating procedure to solve each iteration of SQP much faster than the base case. Both the speed of the algorithm and the final NPV were increased significantly. We evaluate the method by applying it to optimization of control settings in the Brugge field. Brugge is a 3D synthetic model designed by TNO with 20 vertical producers and 10 vertical peripheral water injectors. All of the producers and injectors are smart wells whose downhole chokes must be adjusted to optimize NPV. The total number of completion flow rates to be controlled is 84 at each timestep, with 40 timesteps (every 6 months). There are 1,200 inequality constraints on total well liquid rates and 3,360 nonnegativity constraints on completion liquid rates. There are also inequality constraints on the bottomhole pressure (BHP) for wells at each time period.
- North America > United States > Oklahoma (0.28)
- North America > United States > Texas (0.28)
- North America > United States > California (0.28)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Abstract In the petroleum Industry, the major task is to develop a strategy to maximize the hydrocarbon production within existing physical and economic constrains. The major entities involved in solving this problem are: the oil reservoir and the oil wells. Each of these entities has a number of variables and parameters. Searching for the best configuration of these parameters, for optimum return on investment, constitute an optimization problem. In other to apply the optimization approach, an objective function must be involved. In this study, Net Present Value (NPV) was selected as the profit indicator for return on investment (ROI). Optimization is complicated when more parameters are involved and needed to be optimized simultaneously. Major success will rely on the ability to incorporate effectively, factors (physical and economic) that influence the entire production process in an economic model called the Objective function with or without constraints. A suitable optimization method is also required. In this work, a robust NPV objective function was developed for a reservoir under production. Reservoir, production and economic uncertainties where fully captured. A multivariate nonlinear optimization approach was employed in determining the best configuration of the decision variable and their effect on NPV. Nonlinear optimization methods based on Newton's technique locate the extrema by approximating the objective function with a nonlinear quadratic model. The quadratic model is solved for the stationary point where its gradient goes to zero. If the quadratic model is a good approximation of the objective function, then the stationary point of the quadratic model should be near a stationary point of the objective function. The stationary point of the quadratic model is taken as the new estimate of the objective function's stationary point and the process is repeated until some form of convergence criteria is satisfied. Sensitivity plots where generated for diagnostic purposes as well as to strengthen decision processes. Field application of the NPV model was carried out and reliable results where obtain.
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Management > Asset and Portfolio Management > Project economics/valuation (0.56)
- Management > Asset and Portfolio Management > Field development optimization and planning (0.41)
- Management > Risk Management and Decision-Making > Decision-making processes (0.34)
Effects of Reservoir Parameters and Operational Design on the Prediction of SAGD Performance in Athabasca Oilsands
Nguyen, Huy X. (1 Sejong University - Korea) | Wisup, Bae (1 Sejong University - Korea) | Tran, Xuan V. (2 HCMUT - Faculty of Geology & Petroleum) | Ta, Dung Q. (2 HCMUT - Faculty of Geology & Petroleum) | Nguyen, Danh H. (1 Sejong University - Korea)
Abstract The efficiency of the SAGD process depends on two important factors: reservoir properties and operating conditions. SAGD performance was investigated based on the variables of reservoir properties such as thickness, porosity, permeability, oil saturation, viscosity, rock thermal conductivity, along with operating variables as including preheating, injector/producer spacing, injection pressure, steam injection rate and subcool temperature. In addition, the economic risks associate to the high capital and operation expenditures, and uncertainties of oil and gas prices in the market. In order to manage the uncertainties of oilsands project, we need the quantitative analysis of concerned parameters affecting returns. Then, we can propose optimization design for operating conditions. The previous studies conducted sensitivity analysis and optimization of SAGD performance by classical methods. Therefore, there was a lack of confidence level because they did not determine the significance level of parameters and ignored interactions effects between considered parameters, lead to low efficiency issues in a field operation. Furthermore, the economic models were not comprehensive enough with limited consideration on few factors. These restrictions of classical method can be avoided by applying D-optimal design and response surface methodology to find the best regression model for SAGD performance. There were a total of 75 cases for screening reservoir and operational parameters with the NPV responses based on the D-optimal design. The results showed that reservoir properties have a greatest influence on the SAGD performance with ranking order of porosity, thickness, oil saturation, permeability, viscosity, respectively. The optimization design of operating conditions obtained the maximum NPV when vertical well spacing 9m, injection pressure 5,000kPa.
- North America > United States (0.68)
- North America > Canada > Alberta (0.29)
Abstract Intelligent wells can improve oil recovery, mitigate risks and avoid unnecessary well intervention in petroleum fields. However, there is no consolidated methodology to evaluate the applicability of intelligent wells and to represent them in commercial simulators, which complicates the comparison with conventional wells. Moreover, there are two main modes of operation of intelligent well valves, reactive and proactive; each one can provide different benefits. In general, proactive control seeks maximum oil recovery, but it requires larger computational effort and greater knowledge of the reservoir than the reactive control. This paper presents a comparison between different configurations of intelligent wells with proactive control and mode operation on/off: (1) five-spot configuration with conventional wells (producer and injectors), (2) one intelligent producer and four conventional injectors, (3) one conventional producer and four intelligent injectors and (4) one intelligent producer and four intelligent injectors, in order to compare the different behaviors. The objective of this study is to evaluate the potential of proactive operation for each type of configuration and the benefits of the intelligent injectors and producer acting separately or together, considering the effects on production and costs of intelligent completion. For this, a genetic algorithm was coupled to a commercial simulator to optimize the proactive control and to search the maximum net present value (NPV), determining the optimum operation control for each valve. The case study consists of one heterogeneous reservoir model, light oil and three economic scenarios (pessimistic, probable and optimistic). Results show that the use of intelligent injector and producer wells together, in this case study, can increase of oil production and decrease of water production, although it may not be the most advantageous alternative because of the higher investment. On the other hand, the configuration using only an intelligent producer well (lower investment) is capable of increasing oil recovery sufficiently, therefore making the best investment with intelligent completion, in this case study.
- Europe (1.00)
- Asia (0.68)
- North America > United States > Texas (0.28)