Study On The Uncertainty Forecasting And Decision-making Methods And Their Applications To Evaluate The Reservoir Management Units

Oilfield development has the characteristics of high investment, high risk, and how to obtain the maximum economic effectiveness of exploitation has been a goal pursued by the oil companies. With the current oil and gas resources gradually decreasing and the difficulty of exploitation increasing, oilfield companies widely used the new technologies, and also actively explore the new management mode to promote the delicacy management. Strengthening the management of the oilfield development indicators is an important part of the delicacy management of oilfield development. Owing to the oil reservoir under the deep layers and has the complex internal structure, people’s understanding has certain vagueness for it, coupled with the uncertainty of the development environment, leading to some deterministic methods of forecasting and decision-making are unreliable. By means of the now mature uncertainty theories, such as probability theory, fuzzy set theory, rough set theory and grey system theory, forecasting and optimizing the oilfield development indicators, it can provide the technical support for the oilfield development programming and adjustment; the stage production targets of oilfield development require the uncertainty theories as a tool for the optimal decision.Reservoir management unit (RMU) as the main body of reservoir management, its operation behavior is directly related to the reservoir whether smoothly and orderly developed. The forecasting and decision-making model based on uncertainty theory, by setting up the correspondingly computer programming, can improve the decision-making for RMUs. According to the dynamic of oilfield development, use these techniques of delicacy management to predict the oilfield development indicators or make reasonable planning, and propose the practical and feasible stage production targets. On this basis, formulating a reasonable evaluation system, it can effectively improve the implementation efficiency for the management by objectives. Therefore, the study on the forecasting and decision-making methods of development indicators based on the uncertainty theories is of great significance. In this paper, we mainly do the following:(1) For grey modeling the pretreatment methods of original data sequence were studied. According to the characteristics and the scope of application of grey model, previously processed for the original data sequence, including the processing methods of monotone increasing sequence, monotone decreasing sequence and fluctuation sequence; Grey forecasting methods based on fuzzy number were also studied, including the forecasting methods of interval fuzzy number, triangular fuzzy number and trapezoidal fuzzy number. These methods could effectively solve the forecasting problems of the different forms of time series for the development indicators.(2) The forecasting methods combined the neural network (NN) with the theory of fuzzy sets (FS), rough sets (RS) and grey systems (GS) were studied. The integration of NN and FS can solve the soft forecasting problem, by fuzzifying the deterministic data, it make NN has the better generalization ability; The integration of NN and RS can eliminate the redundant information of training samples to improve the neural network training speed; The integration of NN and GS can weaken the perturbation of training sample data to improve the predictive ability of NN. These methods can solve the multi-input multi-output forecasting problem.(3) The forecasting methods combined the support vector machine (SVM) with the theory of rough sets and grey systems were studied. SVM not only has the good generalization ability as NN, but also can overcome the convergence of local solution and excessive learning; it can be applied not only to sample classification, but also to the multi-input single-output forecasting problems. Combined with RS, SVM can be ruled out the little impact indicators to improve the forecasting accuracy; Combined with GS, the accumulative sequences used as the training samples, SVM can weaken the perturbation of the original sample data.(4) Taking into account the incommensurability between the objective functions, the objective functions can’t be directly linear or weighted sum. The objective function in multi-objective programming was improved, and based on the improved objective function, stochastic and fuzzy programming models were given out, including the expected value model, chance constrained model and dependent-chance model.(5) Uncertainty multi-attribute decision-making (MADM) methods and their application were studied. The comparison method of two interval numbers with probability distribution was studied, and the law of expectation value was put forward, by means of the stochastic simulation, we can get the value of possibility; it was proved that the law of possible degree and advantage degree can be judged by the midpoint of two interval numbers, and we put forward a kind of advantage degree based on the relative entropy method from the complementary and reciprocal angle; considered the fuzzy MADM problems, whose attribute values or attribute weights are fuzzy numbers; MADM problems with the feasible intervals were also studied, and the concept of inclusion degree of interval number was presented, where the feasible interval is the allowable range for an attribute; Rough MADM method was used to determine the determinants of the remaining recoverable reserves grade, and grey MADM methods were used to choose the best development program.(6) Set up the evaluation system of development and management for the oil recovery operation units and the evaluation system of development effectiveness for reservoir management units respectively. By the isomorphic relationship between the traditional analytic hierarchy process (AHP) and the Fuzzy AHP, a kind of consistency transformation was proposed for the traditional AHP. Through the systems engineering approach to determine the hierarchy of evaluation indicators, and the AHP was used to determine the weights of all the evaluation indicators. We formulated the scoring methods of various indicators for the oil recovery operation units and used the data envelopment analysis (DEA) method to evaluate the relative efficiency of development effectiveness for reservoir management units.