Study On Reservoir Prediction Methods Based On Intelligent Computation Algorithms And Their Applications

Reservoir prediction is a basic work to build an accurate geological model ofreservoir, to accurately estimate reserve, to determine proper development scheme, itis not only applicable in oil and gas exploration, but also in guiding development ofthe oil and gas reservoirs, especially complicated subtle reservoirs or lithologicreservoirs, and has become a key scientific issue concerned by both the academic andindustrial circles domestic and abroad. It is difficult to accurately describe thehorizontal distribution of reservoirs parameters by inter-well correlation orinterpolation, the inversion and seismic attribute techniques which joint logging andseismic information are important reservoir prediction method, have been extended tothe oil and gas development stage, become important component of reservoirdescription technology.The essence of inversion technique is to find the solution of a nonlinear objectivefunction. However, the traditional linear inversion methods sometimes plunge intolocal optimum, or depend on the selection of the initial model, thus affecting thereliability of inversion. Intelligent computation methods such as genetic algorithm orsimulated annealing etc. provide a new way to solve reservoir parameters nonlinearinversion problem, but with shortcomings such as converging slowly or falling intolocal optimum, especially for complicated inversion problems. Neural network hasalso been used to build the nonlinear relationship between seismic attributes andreservoir parameters, and predict reservoir parameters. The essence of error backpropagation learning algorithm of neural network is a gradient method, whichconverges slowly to desirable solution, or may be trapped into local optimum.In this dissertation, I propose a number of new ideas to improve the efficiencyand the optimizing performance of nonlinear inversion, the efficiency of neuralnetwork’s learning algorithm for reservoir parameters prediction. I have studiedreservoir parameters nonlinear inversion method based on improved particle swarmoptimization, and new learning algorithm of neural network which hybrids gradient method and particle swarm optimization, in order to predict reservoir parametersthrough multi-attributes. At the same time, I applied the new methods to predictoolitic reservoir, Feixianguan formation, DCH structure of Eastern Sichuan Basin, andobtain the following results:1, in-depth study of the principles of particle swarm optimization, convergenceconditions, parameters setting etc. basic theoretical problem, particle swarmoptimization algorithm updates the solution more purposely, converges more rapidly.A new chaotic inertia weight adjustment strategy is proposed to further improve thealgorithm’s convergence speed. At the same time, I built the multi-parent crossoveroperator and adopt the group hill climbing idea to improve the performance of particleswarm optimization algorithm. On these bases, I develop new reservoir parametersnonlinear inversion methods based on particle swarm optimization algorithm, and themodel simulations and practical data processing results demonstrate that the newmethod is highly efficient with much improved performance over the traditionalnon-linear methods.2, in-depth study of the error back propagation network, radial basis functionnetwork and their learning algorithm. I have applied the particle swarm optimizationas the train algorithm of back-propagation network and radial basis function networks.Fusion gradient method and the particle swarm optimization are developed to form anew hybrid learning algorithm of radial basis function network, and the accuracy andefficiency of new hybrid algorithm have been greatly improved. On this basis, Ideveloped a new reservoir parameters prediction technique by seismic multi-attributewhich based on hybrid learning algorithm neural network, and applied the techniqueto predict porosity parameters which indicates that the hybrid learning algorithm hasshort training time and high efficiency.3, applying new reservoir parameters nonlinear inversion method based onparticle swarm optimization and new reservoir parameters prediction technique ofhybrid learning algorithm neural network to predict oolitic reservoir, Feixianguanformation, DCH structure of Eastern Sichuan Basin. I have drawn the prediction mapfor reservoir thickness, porosity, and storage coefficient. Based on the comprehensivegeological, seismic and log information conclude that the sedimentary facies controlsoolitic reservoir’s level and scope, and the anadiagenesis is the necessary conditionsfor oolitic reservoir development.