Due to the large structural changes and complex lithology,there are many contradictions in fluid identification and fluid interface division of carbonate reservoir in H Oilfield.For gas identification,compensated neutron logging mining effect is effective in some blocks,but not in others.Therefore,it is necessary to analyze the influencing factors of mining effect.Firstly,the lithology and pore structure characteristics of carbonate reservoir in H Oilfield are analyzed.Then the physical characteristics of Layer KA and KB such as pore permeability are analyzed.The response characteristics of mining effects formed by neutron logging and density logging in gas reservoirs are summarized by those curves of several Wells,and the reasons of forming true and false mining effects are analyzed.The density log and neutron log correlation was expounded in theory,and two kinds of logging response correlation equation were deduced.The factors affecting the mechanism of the excavation effect were analyzed from the borehole diameter expanding permeability,pore structure,phy sical property and saturation.Finally,the comprehensive utilization of mining effect judgment analysis method of gas was suggested and the excavation effect of j udge true and false of a few basis was put forward.The fluid identification of K Reservoir in H Oilfield is well completed with this method.Therefore,this method can provide reference for the accurate identification of fluid types in H Oilfield and similar complex carbonate reservoirs.

基于岩心、薄片、地震、测井等资料,通过分析伊朗A油田白垩系Sarvak组生物碎屑灰岩储层内发育的隔夹层类型、地质特征及井震响应特征,系统研究了隔夹层的成因及展布特征.研究表明:A油田Sarvak组储层主要发育泥粒灰岩隔夹层和粒泥灰岩隔夹层,根据隔夹层发育的主控因素,可划分为沉积成因、成岩成因和复合成因.Sarvak组储层内隔夹层的展布特征具有明显的规律:厚度较大、广泛发育的隔夹层主要受沉积作用控制,沉积环境以局限台地相和潟湖相为主;厚度较薄、局部发育的隔夹层主要受压实作用和胶结作用等成岩作用控制.Sar2和Sar7段发育沉积成因隔夹层,平均厚度分别为15 m和5 m;Sar3和Sar8段局部发育成岩成因隔夹层,平均厚度分别为3.25 m和4.5 m左右;Sar4—Sar6段发育大量的复合成因隔夹层,厚度变化较大,总平均厚度为4.8～7.1 m,纵向上广泛发育.

K reservoir is an important oil and gas producing layer of H oilfield in the Middle East, Iraq. The reservoir space is mainly matrix pores and dissolved pores, with a wide range of permeability and poor correlation between porosity and permeability. The reservoir thickness is large, and the stratigraphic heterogeneity is very strong; therefore, it is poor quality to identify carbonate reservoir fluid properties only relying on conventional logging data and traditional logging evaluation methods. Aiming at this problem, the fluid identification work of K reservoirin H oilfield was carried out. By analyzing conventional logging data, it is found that the ratio of deep to shallow resistivity can better distinguish water from hydrocarbon. Based on analyzing morphological characteristics of the total hydrocarbon curve and corresponding reservoir fluid properties, it is found that the gas curve has obvious differences in the morphology of different fluid properties, so it is considered to further classify water and hydrocarbon by using gas curve. It is found that the ratio of heavy hydrocarbon tohydrocarbon gas density index can better classify oil-water layer and water layer. In order to quantitatively characterize the identification process, the identification method of water-oil-water layer gas measurement curve (ECR1) is established. ECR1 greater than 0 is oil-water layer, otherwise, it is water layer. Based on gas wet index, light hydrocarbon ratio and excavation effect, the identification method of gas-reservoir gas measurement curve (ECR2) is established. ECR2 higher than 0 isgas reservoir, and vice versa. The application of this model to 38 small layers in 13 wells of K reservoir in H oilfield shows that the recognition coincidence rate reaches 81.58%, and the recognition accuracy is high, meeting the actual needs of study area. The established ECR model has achieved good application effect in K reservoir of H oilfield, which can provide a certain reference for the subsequent exploration and development of this area, and also provide a reference for fluid identification of similar carbonate reservoirs worldwide.

AVO forward modeling is always constructed by the approximation of Zoeppritz equation in traditional three-term AVO inversion. But the approximation is limited in the case of critical angle and elastic parameters varying severely. Given this problem, we can use the exact Zoeppritz equation to construct the inversion objective function. Because the relationship between P wave reflection coefficient and elastic parameters is nonlinear, the common approach is to use nonlinear optimization algorithm which hasn't been widespread because of the large computation. The alternative is to use generalized linear inversion which uses the linear equation to express the nonlinear relation through the expansion of P wave reflection coefficient into a truncated Taylor series. The GLI can get high accuracy through several iterations in theory. But GLI is unstable sometimes because of the large conditional number of Jacobian matrix. Bayesian inversion combines the prior distribution of model parameters with the likelihood function of the noise to form the posterior distribution of model parameters, which transforms the minimization of objective function into the maximization of the posterior probability distribution. Because of the introduction of the prior information of model parameters, the ill-posed problem can be reduced dramatically. This article combines the ideas of the two methodologies, which uses the idea of GLI to construct AVO forward modeling for improving the accuracy of inverting the large incident angle seismic data and uses Bayesian theory to introduce the model parameters prior information to construct the regularization of inversion objective function for reducing the ill-posed problem of inversion. This algorithm assumes that the prior distribution of the model parameters honors trivariate Cauchy distribution.

Fault sealing evolution is analyzed based on normal stress calculation,and combined with migration pathway pattern division,and favorable areas of fault-lithologic reservoirs of Dainan Formation are predicted in Gaoyou depression.It is concluded that the critical value of fault sealing is 13.8 MPa.Most of the reservoir faults are open in the accumulation period and closed now.However,there is one reservoir fault sealing all the time,which is Shaoshen 1 reservoir of own source type.Two types of migration pathway patterns were divided in theory which are the high-efficient and the low-efficient types respectively.The high-efficient type developed with bottom sand,while the low-efficient without bottom sand.East Lian 12 oil-west Yong 22 oil and east Shao 14 oil are the most favorable areas of fault-lithologic reservoirs of Dainan Formation in Gaoyou depression.Deep Shaobo sub-sag to the northwest of Shaoshen 1 reservoir is the secondary favorable area.

Based on the studies of fault sealing evolution, oil-bearing formation and hydrocarbon generation ability of highly-conductive mudstones in the Dainan Formation, the fault-lithologic reservoir systems of the Dainan Formation in the Gaoyou Sag have been classified into 3 types, including self-sourced, other-sourced and mixed-sourced. The self-sourced accumulation systems mainly locate in the deep Shaobo and Fanchuan Sags, and the Shaoshen1 resrvoir is typical. The other-sourced accumulation systems mainly distribute in the Majiazui, Zhou22 and Shao18 reservoirs. The mixed-sourced accumulation systems are represented by the Lian3, Lian7 and Yong22 reser-voirs. The fault-lithologic reservoirs of the Dainan Formation in the Gaoyou Sag are controlled by2 elements, oil source and migration pathway. The favorable zones for exploration include Huangjue-Majiazui, Lianmengzhuang-Yongan and Caozhuang-Xiaoliuzhuang.

Pore-structure poses great influence on the permeability and electrical property of tight sand reservoirs and is critical to the petrophysical research of such reservoirs. The uncertainty of permeability for tight sands is very common and the relationship between porestructure and electrical property is often unclear. We propose a new parameter δ, integrating porosity, maximum radius of connected pore-throats, and sorting degree, for investigating the permeability and electrical properties of tight sands. Core data and wireline log analyses show that this new δ can be used to accurately predict the tight sands permeability and has a close relation with electrical parameters, allowing the estimation of formation factor F and cementation exponent m. The normalization of the resistivity difference caused by the porestructure is used to highlight the influence of fluid type on Rt, enhancing the coincidence rate in the Pickett crossplot significantly.