Multivariate Interpolation Method In The Optimization Of Oilfield Development Plan And The Cloth Well

It is important to establish field development project and put it into practice, so we must establish and appraise every practical project in detail, then the reasonable plan can be obtained. Although we do our best to make the project perfect, there is wrong with the project which we can not anticipate after the reservoir was exploited because of practical situation or the bug of people and technique, the production performance didn't accord with project design, what's more, because of dense investment, bad risk and the long period of investment, whether the project can be advanced with technology, reasonable with economy and practical with production or not, and the more profits and the less investment, the optimizing the development project is indispensable, and that the important question is global optimum, which is the key in this paper.This study applied two multivariate interpolation algorithms, Least Squares and Kriging, to interpolate a limited number of data obtained from simulation in order to predict the optimal strategies in a field development scheduling project and a waterflooding project with a significant reduction in the simulation efforts required.The two projects were application examples with known answers. The objective of this study was to examine the feasibility and efficiency of multivariate interpolation in solving optimization problems by reducing the simulation efforts required. The net present value was used as the objective function in both projects. The field development scheduling simulation was achieved by an economic model taking account of all the costs and profits during the time period being studied. The waterfooding simulation was achieved by solving the Laplace equation and generating streamlines within the specified pattern. The movements of the waterfront were tracked and then the oil and water production at each producer was calculated.The results obtained by interpolation showed that the multivariate algorithms are able to provide a global sketch of the objective function surface, to avoid possible failure at local optima, and to reach the absolute optimum by refining the grids near the intermediate optimum. The approaches were shown to be practical techniques in optimization, and are likely to be useful in foil scale problems in which simulation costswould be prohibitive for iterative nonlinear optimization methods.