TX 75083-3836, U.S.A., fax 01-214-952-9435. AbstractDetermining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Numerical simulation is often the most appropriate tool to evaluate the feasibility of well configurations. However, since the data used to establish numerical models have uncertainty, so do the model forecasts. The uncertainties in the model reflect themselves in uncertainties of the outcomes of well configuration decisions.We never possess the true and deterministic information about the reservoir but we may have geostatistical realizations of the truth constructed from the information available. An approach that can translate the uncertainty in the data to uncertainty in the well placement decision in terms of monetary value was developed in this study. The uncertainties associated with well placement were addressed within the utility theory framework using numerical simulation as the evaluation tool. The methodology was evaluated using the PUNQ-S3 model, which is a standard test case that was based on a real field. Experiments were carried on 23 history-matched realizations and a truth case was also available. The results were verified by comparison to exhaustive simulations. Utility theory not only offered the framework to quantify the influence of uncertainties in the reservoir description in terms of monetary value but also provided the tools to quantify the otherwise arbitrary notion of the risk attitude of the decision maker. A Hybrid Genetic Algorithm (HGA) was used for optimization.In addition a computationally cheaper alternative was also investigated. The well placement problem was formulated as the optimization of a random function. The GA was used as the optimization tool. Each time a well configuration was to be evaluated, a different realization of the reservoir properties was selected randomly from the set of realizations all of which honored the geologic and dynamic data available from the reservoir. Numerical simulation was then carried out with this randomly selected realization to calculate the objective function value. This approach had the potential to incorporate risk attitudes of the decision maker and was observed to be approximate but computationally feasible.

Determination of the location of new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. In this study a hybrid optimization technique based on the genetic algorithm (GA), polytope algorithm, kriging algorithm and neural networks is proposed. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created on the fly. Performance of the technique was investigated on a set of exhaustive simulations for the single well placement problem and it was observed that the number of simulations required was reduced significantly. This reduction in the number of simulations reduced the computation time, enabling the use of full-scale simulation for optimization even for this full-scale field problem. It was also seen that the optimization technique was able to avoid convergence to local maxima due to its stochastic nature. Optimal placement of up to four water injection wells was studied for Pompano, an offshore field in the Gulf of Mexico. Injection rate was also optimized. The net present value of the waterflooding project was used as the objective function. Profits and costs during the time period of the project were taken into consideration.

Determination of the location of new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. In this study a hybrid optimization technique based on the genetic algorithm (GA), polytope algorithm, kriging algorithm and neural networks is proposed. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use of proxies created on the fly. Performance of the technique was investigated on a set of exhaustive simulations for the single well placement problem and it was observed that the number of simulations required was reduced significantly. This reduction in the number of simulations reduced the computation time, enabling the use of full-scale simulation for optimization even for this full-scale field problem. It was also seen that the optimization technique was able to avoid convergence to local maxima due to its stochastic nature. Optimal placement of up to four water injection wells was studied for Pompano, an offshore field in the Gulf of Mexico. Injection rate was also optimized. The net present value of the waterflooding project was used as the objective function. Profits and costs during the time period of the project were taken into consideration. Introduction Numerical models are detailed and powerful predictive tools in reservoir management. While not perfect they are often the best representation of the subsurface available. Optimization methodologies run these numerical models perhaps thousands of times searching for the most profitable solution to reservoir management questions. Because of the computational time involved optimization methodologies are not used as much as they could be. Various researchers have explored speeding up optimization by either using a speedier evaluation of the objective function (i.e. simpler model or proxy for the full numerical model) or improving the efficiency of the optimization search itself. This paper uses the latter approach focussing on search improvement, yet harnesses some of the techniques often used in proxy development to allow the search to step toward optimality more skillfully. Researchers have looked into optimization of well placement and rate using numerical simulation. Beckner and Song1 formulated the problem as a traveling-salesman problem and used simulated annealing to optimize well location and drilling schedule. Bittencourt and Horne2 investigated optimization of well placement using a hybrid of genetic algorithm (GA) and polytope method. Güyagüler and Gümrah3 optimized production rates for a gas storage field using GAs. Aanonsen et al4 coupled a CPU efficient reservoir simulator with an optimization algorithm and made use of a kriging proxy to find optimum well locations. Pan and Horne5 also used kriging to decrease the necessary number of simulations required to optimize well location. Rogers and Dowla6 and Centilmen et al7 used neural networks as a substitute for numerical simulation. We have been exploring search improvements in GA8 to decrease the total number of individual simulations required for convergence of the optimization problem. The GA is popular for its strengths in avoiding suboptimal solutions, freedom from requiring derivatives, and ease of parallelization. The GA was chosen over other popular heuristic search algorithms, such as simulated annealing, because of the concept of population and the greater ease of parallelization. Parallelization obviously has the potential to speed calculations. The concept of population integrates well with the formulation of the search improvements of this work. Some of the same traits that make the GA robust and powerful also make it slow and inexact in refinement of the solution. The GA typically has rapid initial progress during the search, but has problems locating the final optimal solution.

Optimal placement of oil, gas or water wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, as well as economic parameters. An optimization approach that enables the evaluation of all these information is presented. A hybrid of the genetic algorithm (GA) forms the basis of the optimization technique. GA operators such as uniform, single-point, two-point crossover, uniform mutation, elitism, tournament and fitness scaling were used. An additional operator that employs kriging is proposed. The GA was hybridized with the polytope algorithm, which makes use of the trends in the search space. The hybrid algorithm was tested on a set of mathematical functions with different characteristics in order to determine the performance sensitivity to GA operators and hybridization. Simple test cases of oil production optimization on 16×16 simulation grids with known optimum well locations were carried out to verify the hybrid GA results. Next, runs were carried out for a 32×32 problem. The locations of a production and injection well were optimized in the case of three existing producers. Exhaustive runs were carried out for these cases to determine the effects of the operators, hybridization and the population size on the performance of the algorithm for well placement problems. Subsequently, the optimum configuration of two injection wells were determined with two existing producers in the field. It was observed that the hybrid algorithm is able to reduce the required number of simulations substantially over simple GA. [S0195-0738(00)00502-1]

Externally coupled workflows that rely on exchanging inflowperformance relationships (IPRs) at the coupling points, such as those between reservoir and surface-network simulators, may exhibit oscillations because of the IPR calculated at the beginning of a timestep not being representative of the IPR at the end of the timestep. One solution is to have an implicitly coupled reservoir/surface system. This is often impractical because the reservoir and the surface network may be modeled using different applications, or the resulting coupled system may become too large and too complex to solve implicitly because of processing time and convergence issues. We propose the calculation of multipoint IPRs obtained by solving near-well subdomains for the subsequent timestep. A flexible reservoir-simulation architecture enables the dynamic creation and simulation of near-well subdomains at run time. Subdomains are created automatically within the vicinity of the well or may be defined dynamically from the pressure gradient. These near-well-subdomain simulations are embedded within the full-field simulation and extract all the required model properties (pressure/volume/temperature, rock) from the full-field model. The most recent fluxes from the global solution are used as boundary conditions for the near-well subdomains. In this paper, the subdomain IPRs are used within reservoir/network coupling workflows for which traditionally calculated IPRs result in oscillations and high errors. Sensitivity analysis is carried out on the extent of the subdomains and the size of the coupling timestep. A real field case is used to show that subdomain IPRs result in smooth pressure/ rate profiles as opposed to the oscillatory profiles obtained from explicitly calculated IPRs and that they also help reduce balancing errors between reservoir and surface models.

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Claims include (i) providing a pair of horizontal wells, separated by a distance of approximately 3 to 8 m, each with a liner and an additional tubing string extending through the full length of the liner, (ii) conductive heating of the interwell region by circulating steam in both wells for an initial period of time, (iii) subsequent injection of steam in the upper well through both the long tubing string and at the heel of the annulus at a constant pressure that is less than fracture pressure, and (iv) simultaneously, producing subcooled fluids in the lower well, where the subcool is maintained by controlling the production rate so that the fluid state remains somewhat below saturated steam conditions, thereby enforcing a column of steam-free liquid just above the lower producer.

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