Research On The Optimization Of Oilfield Enterprise Logistics System And Economic Evaluation

There are many problems in the development of petroleum industry in our nation, for example, the Petroleum storage is not sufficient, high cost per ton petroleum, thus, it is necessary to find a method for reducing cost and improving economic benefit. As a large company consisted of petroleum exploration, development, refining and petro-chemical industry, the oilfield enterprises have done a lot of effort for control the cost expenditure in the exploration and development process in order to improve their competitiveness in the international market. While since the space for reducing the costs of crude oil exploration, development, mining and other production cost becomes smaller and smaller, fostering new points of benefit growth becomes a very important way for keeping lager oilfield enterprises’sustained competitive advantage. Recently, the method of logistics and distribution optimization has been adopted by many oilfield enterprises for reducing cost and improving economic benefit in order to improving their competitiveness. Implementing the logistics system and distribution process optimization can effectively reduce the storage and cost for material distribution, thus becomes the third benefit resource of large oilfield enterprises. Under this background, this thesis, based on the logistics economics and optimization related theories, takes material requirement forecasting, warehouse location, distribution optimization, which are the three important parts of the oilfield logistics system, as the main study objects for studying the optimization problem of oilfield logistics system.This thesis firstly presents the research background, purpose and meaning of oilfield logistics system optimization and economic evaluation. Then, we review the main research results and obtained some interesting research directions. At last, from the above review process, we determine our four main research contents, study methods and technology roadmap. The main research conclusions of this thesis are shown as follows:First, for material requirement forecasting of oilfield enterprises, this thesis firstly divide the main materials required by oilfield enterprises into different categories according to their own demand laws, and then construct different material demand forecasting models including Genetic Algorithm optimized Wavelet Neural Network (GA-WNN). This Wavelet Neural Network (WNN) is constructed by combining the wavelet analysis and neural work. The WNN obtains strong function approximation ability, especially on the catastrophe points, through adjusting the wavelet parameters, thus it can make up the disadvantage of falling into local optimum of traditional Artificial Neural Network (ANN). In order to improve the training speed and forecasting precision and reduce fluctuation of WNN, this thesis optimizes the initial weights and wavelet parameters using genetic algorithm. Moreover, the weights of WNN are updated through a variable learning rate. At last, this thesis take one oilfield enterprise affiliated with China Sinopec group as a case to illustrate in detail the using methods of these material forecasting models. Experiments results show that the above material forecasting methods can accurately forecast the 71.12% (ratio of capital of forecasted materials/capital of all materials) materials, and the average errors are lower than 16%. Research conclusion can effectively improve the accuracy of the main material demand forecasting so as to guide the enterprises’ purchasing behavior, reduce material storage and guarantee material supply.Second, for warehouse location problem of oilfield enterprises, since it is a mid-long term decision making, thus before the determination of the warehouse, there are many oil wells’ positions have not determined. Therefore, in order to improve the economic benefit and service level of the warehouse, this thesis firstly, based on the production plan of the oilfield enterprise, forecasts the number and positions of the oil wells using the Monte Carlo random forecasting method. Based on the forecasted oil well information, we can obtain the material demand information of all oil wells. Then, in order to obtain the feasible candidate locations of warehouse, this thesis firstly construct a two-level successive location model, and build an exact algorithm. For improving the solving efficiency of large scale models, we construct an improved genetic algorithm through nesting the Fmincon function of Matlab software into traditional genetic algorithm, in which the local optimal solutions obtained through the Fmincon function will be put into the population of genetic algorithm for evolution. Therefore, this improved genetic algorithm not only have the advantage of global searching ability, but also have strong local searching ability. After obtaining the solution of the two-level successive location model, we take the solution point as the centre of a circle and draw a rotundity using a variable radius (take each kilometer as the unit), until the rotundity includes enough candidate warehouse locations (in the case study of this thesis, when the radius is set to 30 kilometers, the rotundity includes 7 candidate warehouse locations). In order to well illustrate the application process of the above constructed models, we take the one oilfield enterprise affiliated with China Sinopec group as a study case.Third, for the oilfield’s warehouse location evaluation, we firstly divide all of factors related to warehouse location into two categories according to quantitative and qualitative standards. Then, this thesis constructs a set of scientific and reasonable indices for oilfield’s warehouse location evaluation. At last, we conduct the evaluation process using the Analytic Hierarchy Process (AHP) evaluation method and obtain the satisfied solution through sorting the scores of all candidate warehouse locations. In order to well illustrate the application process of the above constructed evaluation models, we take the one oilfield enterprise affiliated with China Sinopec group as a study case.Forth, for the material distribution of oilfield enterprise, since the roads between the warehouses and material demand points are complex, thus, this thesis firstly divide the roads between the warehouses and material demand points into different categories through giving different weights for different kinds of roads. Then, we, taking the village or township as the unit, analyze the corresponding relation between the unit and each forecasted oil well and take the village or township as the material demand point in order to calculate the material distribution cost. At last, in order to improve the operability of this method, we design a Graphical User Interface. In order to well illustrate the application process of the above constructed models, we take the one oilfield enterprise affiliated with China Sinopec group as a study case.Fifth, this thesis firstly, based on the logistics economics theory, has done the economic evaluation for the oilfield’s logistics system, and analyzed the effects after optimization. Then, this thesis takes the related cost factors and savings of the logistics system after optimization as the input and output indices, respectively, and constructs an input-output economical evaluation model based on the Data envelopment Analysis method. In order to well illustrate the application of this evaluation model, we take one oilfield enterprise from the Sinopec group as a case to present the application process. This conclusion can help oilfield enterprises change their management views of logistics system, providing some new thoughts for cost control and management in oilfield enterprises.At last, we conclude this thesis, and point out some future research directions.