CAJ | 학술논문

为了利用三维地震资料开展冲积扇低孔、低渗砂砾岩油藏产能指标预测研究，选取准噶尔盆地西北缘Y地区三叠系百口泉组油藏为靶区，在砂砾岩厚度与孔隙度等常规储层预测基础上，精细剖析影响油藏产能的渗透性和含油性等因素，将老区初期平均月产量数据引入三维地震反演过程中，采用层层深入、逐步逼近的思路开展油藏产能指标预测研究，总结形成了“特征曲线反演找准砂砾岩、孔隙度反演找准高物性砂砾岩、自然电位反演找准渗透性砂砾岩、电阻率反演找准含油砂砾岩、多体融合预测油藏产能指标”的研究流程。最终以月产能指标为硬数据，以波阻抗、孔隙度、电阻率和自然电位反演数据体及时间域构造为训练样本，利用神经网络模拟得到油藏产能指标数据体。研究结果表明，预测月产能指标与油井初期平均月产油量为正相关，相关系数R2＝0.9487，老井初期平均月产量大于300 t的预测误差小于10％。产能指标数据体蕴含岩性、物性、含油性和渗透性等控制油气分布的多种信息，依据Y地区相应研究成果建议部署的3口评价井试油产量均在5t/d以上，验证了该产能指标预测技术的准确性与实用性。
위료이용삼유지진자료개전충적선저공、저삼사력암유장산능지표예측연구，선취준갈이분지서북연Y지구삼첩계백구천조유장위파구，재사력암후도여공극도등상규저층예측기출상，정세부석영향유장산능적삼투성화함유성등인소，장로구초기평균월산량수거인입삼유지진반연과정중，채용층층심입、축보핍근적사로개전유장산능지표예측연구，총결형성료“특정곡선반연조준사력암、공극도반연조준고물성사력암、자연전위반연조준삼투성사력암、전조솔반연조준함유사력암、다체융합예측유장산능지표”적연구류정。최종이월산능지표위경수거，이파조항、공극도、전조솔화자연전위반연수거체급시간역구조위훈련양본，이용신경망락모의득도유장산능지표수거체。연구결과표명，예측월산능지표여유정초기평균월산유량위정상관，상관계수R2＝0.9487，로정초기평균월산량대우300 t적예측오차소우10％。산능지표수거체온함암성、물성、함유성화삼투성등공제유기분포적다충신식，의거Y지구상응연구성과건의부서적3구평개정시유산량균재5t/d이상，험증료해산능지표예측기술적준학성여실용성。
In order to use 3D seismic data for productivity index prediction of alluvial fan coarse-grained clastic reservoirs with low porosity and low permeability , we chose the Triassic Baikouquan Formation reservoir in Y-region at the north-western margin of Junggar Basin as a case .Based on traditional reservoir prediction such as thickness and porosity of coarse-grained clastic reservoirs ,we analyzed in detail factors influencing permeability and oil-bearing properties ,and in-troduced the average monthly production data at the early stage of development into 3D seismic inversion to predict the productivity index .The following work flow was established:‘finding coarse-grained clastic reservoirs through typical curve inversion ,finding high quality coarse-grained clastic reservoirs through porosity inversion ,finding permeable coarse-grained clastic reservoirs through spontaneous potential inversion , finding oil-bearing coarse-grained clastic reservoirs through resistivity inversion ,and predicting reservoir productivity index with the combination of several methods ’ .A pro-ductivity index cube was finally generated through Neural Network modeling by using the monthly productivity as hard da -ta and wave impedance,porosity,resistivity,spontaneous potential inversion data cube and time domain structure as trai-ning samples.The result shows that there is a positive correlation (R2 =0.948 7)between the predicted monthly produc-tivity and initial average monthly production .For wells with an initial monthly average production more than 300 ton,the error of prediction is less than 10%.The data cube contains various information controlling hydrocarbon distribution ,such as lithology ,reservoir property ,oil-bearing property and permeability .The oil production of three appraisal wells deployed based on this research in Y-region reached more than 5 ton per day,which verified the accuracy and practicability of this productivity index prediction technology .