Research On Gtutenite Reservoirs With GA-CM Hybrid Optimization Log Interpretation Method In Lishu Fault Depression

As unconventional oil resource owning to special sedimentation and tectonization, glutenitereservoir gets more and more attention in oil exploration. However it is hard to divide lithologyand set up the interpretation model accurately, due to the reservoir character of deep burial,complex lithology, low porosity and permeability, and high heterogeneity.The optimal log interpretation paves a way for the evaluation of complex lithologyreservoir, as it can take full advantage of the logging information, and measurement error,logging response function error, geological information and working experience, lying on thegenerality geophysical inversion theory, and using optimization method, and statisticsprobability theory and error theory and quality control technology, then find the mostreasonable result from all the possible solutions.In this paper, the relationship among lithology and oiliness and physical property andelectric nature of glutenite is studied, and a multicomponent model is proposed for welllogging interpretation. Similar with multiple mineral model, the reservoir matrix isdecomposed to many parts, however the glutenite lithology is complex, the lithic fragment isalso multicomponent, as a result, the lithic fragment is take as a whole part, and similar forplagioclase and potassic feldspar, so the matrix is make up of quartz and feldspar and lithicfragment. As to simplify the calculation, the linear well logging response equation is used.As a core of optimal log interpretation, mathematical algorithm is deterministic part, theGenetic Algorithm is a kind of heuristic method and is applied extensively these years.However the GA method is easily converge earlier, so the paper proposed a method ofcombining the Complex Method with GA and forming a Hybrid Genetic Algorithm. TheComplex Method is a local search approach which has no require of gradient information, andit is easily used.In this paper, genetic algorithm is first introduced to optimal log interpretation to get thereservoir parameters, such as porosity and shale content and mineral component content, whichmatched the core data well, however many catastrophe points were observed on the curves.After the CM is produced into GA, the catastrophe points disappeared and the theory welllogging curves were essentially coincident with the actual ones. Finally the method is appliedin calculating rock-electro parameters, then the desired water saturation is obtained.