Study On Log Interpretation Method For Fuyu Formation In Chao-chang Of Daqing Area

With the development of exploration and exploitation in Daqing peripheral oilfield, Fuyuformation in Chao-chang area of Daqing peripheral oilfield has become the key block formore exploration. This region dominated by low porosity and permeability reservoirs andincluded medium porosity and permeability reservoirs. Since the reserves have widedistribution, great variation in buried depth and strong heterogeneities, the pore structure ofthe reservoirs is complex. Besides, the action of additional conductivity of clay is significantbecause of high shale content. All of these reservoir characteristics lead to the difficulty oflogging interpretation in this region.With coring data, well testing Data and well logging data, the four properties relations ofreservoirs are studied, laying a good foundation for identifying reservoir fluid property andestablishing parameters interpretation models. According to the complexity of pore structurein Chao-chang area, with equivalent volume model of clean water sand and parallelconductance theory, formation factor formula is derived. With the formula, a relationshipbetween micro pore structure parameter pore tortuosity and rock resistivity is established. Inthis paper, structure coefficient is introduced to describe the level of pore tortuosity in theformation factor formula, so a new formation factor equation is established. With rockresistivity and mercury injection experiment, the reasonableness and influence factors of themodel are analyzed. It is proved that there is clear power correlativity between structurecoefficient and formation factor. The result shows that the more complex rock pore structureis, the greater structure coefficient is, and the higher formation factor becomes. Byintroducing the proposed formation factor into the DOLL equation, the problems of porestructure and additional conductivity of clay are resolved.Based on grain size and mercury injection experiment data, empirical formulas to calculateshale content, porosity, permeability and capillary bound water saturation are established. Themethods to calculate processing parameters are given by rock resistivity scale equations andoptimization technique. A good consistency between calculated water saturation and analyzedwater saturation in sealed coring well shows that the proposed model does contain effect ofpore structure on rock resistivity in some degree. In addition, artificial neural network ofoil-water identification is established. The application example shows that the model canidentify oil-water effectively and improve the accuracy and efficiency of oil-water bedidentification.