Baehyun Min

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

This paper presents a new Pareto-based evolutionary model incorporated with preference-ordering and objective-dimension reduction to improve the multi-directional searches for multi-objective problems.It induces a convergence toward the Pareto-optimal front by adjusting aspiration levels allocated to objectives and by excluding redundant objectives during optimization.Its usefulness was validated for multi-objective test problems comparing to conventional single- and multi-objective optimization models. This study investigates the coupling effects of objective-reduction and preference-ordering schemes on the search efficiency in the evolutionary process of multi-objective optimization. The difficulty in solving a many-objective problem increases with the number of conflicting objectives. Degenerated objective space can enhance the multi-directional search toward the multi-dimensional Pareto-optimal front by eliminating redundant objectives, but it is difficult to capture the true Pareto-relation among objectives in the non-optimal solution domain. Successive linear objective-reduction for the dimensionality-reduction and dynamic goal programming for preference-ordering are developed individually and combined with a multi-objective genetic algorithm in order to reflect the aspiration levels for the essential objectives adaptively during optimization. The performance of the proposed framework is demonstrated in redundant and non-redundant benchmark test problems. The preference-ordering approach induces the non-dominated solutions near the front despite enduring a small loss in diversity of the solutions. The induced solutions facilitate a degeneration of the Pareto-optimal front using successive linear objective-reduction, which updates the set of essential objectives by excluding non-conflicting objectives from the set of total objectives based on a principal component analysis. Salient issues related to real-world problems are discussed based on the results of an oil-field application.

Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential

Abstract This article presents an efficient approach to determine the optimal drilling location for maximizing the cumulative production without the need for a reservoir simulation, of which scheme is based on artificial neural network incorporating the productivity potential. A reservoir simulator can provide an accurate result, but is sometimes inefficient due to the enormous computing requirements. The typical artificial neural network scheme used in multiwell placement shows lower predictability as the size of the input data increases. This work introduces the productivity potential that merges various reservoir properties, such as the permeability, porosity, and saturation, and integrates it into an artificial neural network. The cumulative production is compared with the result of the reservoir simulator to determine the accuracy of the developed method. The efficiency of the conventional artificial neural network is improved by the proposed model, as well by using the productivity potential instead of a lot of separate inputs. The predictability is verified by determining the drilling location in the same way as that of the reservoir simulator in the case of a single infill well. The stability is confirmed by its ability to produce a reliable result even as the number of input data increases.

Development of a robust multi-objective history matching for reliable well-based production forecasts

This article presents a dynamic reservoir characterization using a new multi-objective optimization algorithm to quantify the reservoir uncertainties in history matching. The proposed method formulated Pareto-optimality with preference-ordering to derive multiple trade-off history-matched reservoir models for probabilistic production estimation. The integration of linear programming with multi-objective genetic algorithm enhances the efficiency of a multi-directional search by prioritizing the reservoir models that satisfy the aspiration levels on the discrepancy between the observed and the calculated production data. The preference levels are automatically adjusted in correspondence to the quality of the reservoir models for facilitating the model update process during optimization. An oil-field application result indicates the method outperforms the conventional multi-objective optimization method in terms of the relative average error for the production data despite a small loss of diversity-preservation among the reservoir models.

An Optimal Operation Strategy of Injection Pressures in Solvent-aided Thermal Recovery for Viscous Oil in Sedimentary Reservoirs

Injection pressure is a key control parameter, which determines the amount of solvent and steam mixture consumed and its optimum level dominates the economic efficiency of solvent-aided thermal recovery in oil sands reservoirs. The authors determine the optimal strategy of determining operating pressures to achieve the maximum economic value; the scheme is based on an artificial neural network (ANN). The multilayer perceptron using backpropagation minimizes the objective value including bitumen production, steam injection, solvent retention, commodity price, and manufacturing cost. The numerical approach integrating with ANN results in accurate prediction similar to the time-consuming reservoir simulation. The application to the Athabasca oil sands reservoir confirms the enhanced results compared with the constant injection scenario and proposes the optimal schedule of injection pressures that repeats the increment and the decrement until reaching the same pressure level between the injection and the production well. This model maintains the consistency of the peak production rate on account of latent heat despite reducing the cycle amplitude. The developed model could be applicable to economically designed injection scenarios without any modification of production facilities.

Compound Real Options Incorporated With a Stochastic Approach for Evaluating an Uncertainty in Petroleum Exploration

The article presents compound real options to examine the effects of uncertainties on the strategic decision-making in petroleum exploration. The evaluating approach is based on real option valuation but incorporated with decision tree and stochastic discounted cash-flow to demonstrate the changeable environment. Various uncertainties are employed to represent the characteristics of exploration, related to market and technical conditions. The model yields the reliable evaluation and investigates the exploring characteristics efficiently. The more uncertain the project is, the more volatile it is by including the jumping terms in price changes, non-deterministic production profiles, and fiscal terms successively. The model can measure the uncertainties quantitatively by reflecting them on the profitability. It confirms that petroleum exploration is very volatile so that the adequate managing decision plays an important role in economic evaluation.

Multi-Objective History Matching Allowing for Scale-Difference and the Interwell Complication

This study presents a new multi-objective history matching model to predict the individual well performance. Typical single-objective history matching, reducing the linearly averaged form of different-scaled objectives, has not covered the individual well performance properly. Previous multi-objective history matching, which could demonstrate the individual performance, shows the poor applicability as the number of objective function increases. This work aims to develop the accurate and diversity-preserved methodology to accomplish the global optimization. The scheme consists of dynamic goal programming and successive linear objective reduction incorporated with non-dominated sorting genetic algorithm-II. Dynamic goal programming grants priorities to solutions satisfying the expectation values for the objective functions with goal adjustment. SLOR removes redundant objective functions at the fitness evaluation in genetic algorithm. For the case study of waterflood history matching, the model is less sensitive to the form of objective functions and gridblock size. This study proves that reflecting relativity of different performances is able to improve prediction ability of the conventional single- and multi-objective approaches. The model provides a reliable range of uncertainty from diversity-preserved concept. The developed multi-objective optimization algorithm can easily apply to solve the convergence problem and the unrealistic estimation caused by scale-difference and the complication among multi-objective functions.

Optimal design of hydraulic fracturing in porous media using the phase field fracture model coupled with genetic algorithm

We present a framework for the coupling of fluid-filled fracture propagation and a genetic inverse algorithm for optimizing hydraulic fracturing scenarios in porous media. Fracture propagations are described by employing a phase field approach, which treats fracture surfaces as diffusive zones rather than of interfaces. Performance of the coupled approach is provided with applications to numerical experiments related to maximizing production or reservoir history matching for emphasizing the capability of the framework.

Multiphase Flow Simulation for In Situ Combustion to Investigate Field-scale Hydraulic Heterogeneity and Air Injection Rate Affecting Oil Production

The article presents numerical analyses on the effects of field-scale hydraulic heterogeneity and air injection rate for production behavior of in situ combustion. Hydraulic heterogeneity is an essential reservoir property on determining the movement of a combustion front while the air injection rate is a key operational factor on maintaining the combustion front. Hydraulic heterogeneity affects significantly occurring viscous fingerings but may not be critical at the averaged cumulative production. The lower air injection rate results in the extensive combustion front and the large amount of ultimate recovery if the front is maintained before it reaches a production well.

Operation Strategy of Steam and Gas Push in the Presence of Top Water Thief Zone

The paper presents the injection strategy of steam and nitrogen for one kind of non-condensable gases on SAGP (Steam And Gas Push) process in the presence of top water-bearing formation in heavy oil reservoir, which optimizes the energetic value using ANN (Artificial Neural Network). Top water thief zone is problematic to cause the water influx and the heat loss, and thereby reduces significantly the bitumen production efficiency. The valid amount of both steam and nitrogen is essential in order to accomplish the minimum energetic value and the production efficiency. The authors verify the prediction accuracy of the developed ANN by showing an intensive correlation with the time-consuming reservoir simulations. The optimal scenario results the increment of bitumen recovery up to around 16.1% and the decrement of objective function up to 13.72% compared to the SAGP with constant injection strategy. Before the chamber reaching the top water-bearing formation, nitrogen concentration maintains low level for chamber enlargement while both high pressure and nitrogen concentration are used to block the water influx into the heated zone after contacting the thief zone. In the period of late production, it reduced the injection pressure to enhance the thermal efficiency.

Production System Optimization of Gas Fields Using Hybrid Fuzzy-Genetic Approach

This paper (SPE 100179) was accepted for presentation at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, 12–15 June 2006, and revised for publication. Original manuscript received for review 15 April 2007. Revised manuscript received for review 30 July 2009. Paper peer approved 29 September 2009. Summary The design of production systems of gas fields is a difficult task because of the nonlinear nature of the optimization problem and the complex interactions between each operational parameter. Conventional methods, which are usually stated in precise mathematical forms, cannot include the uncertainties associated with vague or imprecise information in the objective and constraint functions. This paper proposes a fuzzy nonlinear programming approach to accommodate these uncertainties and applies it to a variety of optimization processes. Specifically, the fuzzy -formulation is combined with a hybrid coevolutionary genetic algorithm for the optimal design of gas-production systems. Both the multiple conflicting objective and constraint functions for production systems of gas fields are formulated in a feasible fuzzy domain. Then, the genetic algorithm is used as a primary optimization scheme for solving the optimum gas-production rates of each well and the pipeline segment diameters to minimize the investment cost with a given set of constraints in order to enhance the ultimate recovery. The synthetic-optimization method can find a global compromise solution and offer a new alternative with significant improvement over the existing conventional techniques. The reliability of the proposed approach is validated by a synthetic practical example yielding more-improved results. This method constitutes an offering of a powerful tool for cost savings in the planning and optimization of gas-production operations.