Changhyup Park

A new automated scheme of quadrilateral mesh generation for randomly distributed line constraints

This paper presents a new automated method of quadrilateral meshes with random line constraints which have not been fully considered in previous models. The authors developed a new looping scheme and a direct quadrilateral forming algorithm based on advanced front techniques. This generator overcomes the limitations of previous studies such as line constraint, unmeshed hole and mesh refinement. A qualitative test reveals that our algorithm is reliable and suitable at the field needed for very accurate results. The developed direct method to handle line-typed features automatically makes the multiple discretizations without any user interaction and modification.

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.

Prediction of nonlinear production performance in waterflooding project using a multi-objective evolutionary algorithm

The paper presents a multi-objective evolutionary algorithm applied to history matching of waterflooding projects, which is to search a feasible set of geological properties showing the reliable future performance. Typical history matching has concentrated on single objective function with linearly weighted terms, even as a realistic field includes many wells and well measurements in time and type. The optimal solution is sensitive to weight factor and competing match criteria of individual term in the objective function often reduce the likelihood of finding an acceptable match. The unacceptable error at a specified well can be observed in a heterogeneous reservoir where shows nonlinear well performances. To overcome the inaccuracy, a new history matching approach is developed that allows the performance characteristics of the whole wells. Individual well performance is optimized separately using genetic algorithm coupled with non-dominated sorting and diversity preservation. The fitness is sorted along to the proximity and then the diversity is added by examining the crowding distance as the approach to arrive at the global optimum. Waterflooding is demonstrated in a heterogeneous oil reservoir with multiple production wells. The predictability of unknown future production performance is compared with that of single objective function, the conventional history matching method. The model represents individualized well-performance more accurately than the conventional history matching. It improves a certainty of the conventional model by showing small error range. The selection of adequate set of reservoir properties is possible among the feasible solutions unlike the conventional model. The developed method can be applied as a useful tool for uncertainty analyses in waterflooding projects.

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.

Numerical Analysis of Diffusion in Discrete Fracture Networks with Fractal Geometry by Using Pressure Transient Data

This article presents numerical analyses on diffusion trend for the fractured media with fractal properties by pressure transient data. The authors develop a discrete fracture model with random fracture pattern and a power-law length distribution, which has not been fully considered in the previous fractal modeling. From the model presented, the fractal dimension and conductivity index is determined, and the pressure behavior is analyzed for the range of length exponent (a) of power-law from 1 and 3. Various fractal geometries are examined depending on the morphology of fractures. A single fractal characteristic is observed in the media dominated by long (a < 2) or small fractures (a > 3), and two distinct fractal dimensions in pertinently mixed system of long and small fractures (2 < a < 3). Also, pressure transient behaviors in the mixed system show their unique characteristics, corresponding to each fractal dimension respectively. It is shown that diffusion effect is relatively small in the early time influenced by a few long fractures, and becomes larger in the late time by long and small fractures.

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.

A Fuzzy Model Integrated with Electrofacies Characterization for Permeability Prediction

A new integrated model of statistical pre-treatment and fuzzy logic is developed to predict unknown permeabilities from well logs. The log treatment based on factor analysis and clustering characterizes statistically electrofacies that are the input variables of fuzzy logic. The applicability is verified in the heterogeneous carbonate reservoir, which shows the complex and anti-persistent permeability sequence. Electrofacies helps to improve the predictability of fuzzy prediction by showing the reduction of the prediction error up to 2.3%. The estimation is stable even as the number of input data decreases in contrast to that of traditional methods without the pre-treatment. The model provides a practical tool for predicting the wellbore permeability in a heterogeneous reservoir where it is difficult to establish a functional relationship between permeability and well log.

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.