Seismic Sedimentology Research On Sand Group Ⅻ Of The3rd Member Of Qingshankou Formation In Qianbei Area, Qian’an Oilfield

Qian’an area is located in the central and northeastern part of Changling depressionof the Central Subbasin in Songliao Basin, which comprises mainly Qianbei Area,Qian’an Old District and Daqingzi Oilfield. Qianbei oilfield is located on the north ofQian’an Subdepression, on the south of Daan Depression, and it takes Daan-HongganBench and Huazijing Bench as west and east boundaries. In the mass, it is awest-inclined homocline, and it is one of the most important areas which containabundant lithologic reservoirs. The strata here are of good completeness. SandgroupXII of the3rd Member of Qingshankou Formation(Qingsan Member) is the studyinterval which is constituted mainly by interbeddings of different thicknessesincluding dark grey mudstones, purplish red mudstones, silty mudstones, grey muddysiltstones, grey siltstones and brown siltstones. This interval is in conformity contactwith the lower strata.The seismic sedimentary research was based on seismic data, along with welllogging, well drilling, cores and other materials. Using accurate structuralinterpretation, seismic attribute analysis, spectrum analysis, sedimentary micro faciesresearch, seismic inversion and other technologies, the lithology, lithologic facies,physical properties of the target reservoirs were studied and favorable areas werepredicted. Qianbei Oilfield is covered by3D working field. Aiming at two3Dworking fields of different year,3D sectors connecting analysis and interpretationhave been carried out. Characteristics of a single sandbody, being thin, varyingquickly in plane and being of low permeability, brought great difficulties andchallenges to the work of prediction the favorable areas. Hence, a seismic sedimentaryresearch was strongly expected to be carried out. This study was on the basis of3D seismic data, collectively using well logging,well drilling and core analysis materials, guiding by seismic sedimentology,geophysics, reservoir sedimentology, sequence stratigraphy and some other theories.Accurate structure analysis was operated first. Using accurate synthetic seismogram,multi-well calibration and time-frequency analysis, the sequence stratigraphicproperties were studied, and further, the distribution and strikes of the faults of thestudy area were studied according to coherentbody, dip body and ant bodytechnologies, and also, the structural form was confirmed. Guiding byfacies-controlled theory, along with seismic attribute analysis, stratum-slicing andattribution optimized cluster analysis, favorable areas were divided. The distributionof sedimentary micro facies of each small layer was studied by analyzing the singlewell facies of well logging materials, combining the result of seismic attributeanalysis. Finally, the lithologies, physical properties and other characteristics of planedistribution were studied, inner properties inside sedimentary bodies were furtheranalyzed, and the distribution of favorable areas which resulted in fine effects waspredicted by using constraint wave impedance inversion, multi-attribute inversion.(1) Study the sequence stratigraphic properties using accurate synthetic seismogram,multi-well calibration, time-frequency and other methods combining well logging andseism. Carry out accurate seismic interpreting work using3D visualization, timeslices, connecting well arbitrary line coherent bodies, dip bodies and ant bodies.(2) Guiding by facies-controlled theory, along with seismic attribute analysis,stratum-slicing and attribution optimized cluster analysis, divide the favorable areas.Carry out the sedimentary microfacies research on every layers of the study intervalaccording to the results of seismic attribute analysis. Parameters such as the thicknessof the sandbodies of the reservoirs in the study interval have fine correlationships withamplitude and frequency attributes. Depict accurately the distribution characteristicsof reservoir facies by extracting multi attributes and optimization of the technology ofcluster analysis.(3) Study the plane distribution characteristics of the lithology and physicalproperties of the reservoirs in the study interval. Further analyze the internal characteristics inside the sedimentary bodies and predict the favorable area’sdistribution. Calibrate accurately every sandgroups and small layers, establish propergeologic models and operate well logging constraint inversion, according to thepreprocessing of well logging materials and accurate calibration of reservoirs.Inverse the data body combining with wave impedance, and predict the distribution ofthe characteristics of the reservoir parameters using PNN nonlinear neural networkmulti-attribute seismic inversion. Its correlation coefficient reached0.8and it resultedin a fine effect.This result has been widely spread and applied in the Sandgroup XII of QingsanMember, which matched well with the practical drilling. And also, it has finelydirected the exploration and exploitation of this area proving that this research has acertain extent of directive meaning.