圈闭含油气性评价研究进展

在油气勘探过程中,圈闭含油气性评价越来越受到重视。评价圈闭含油气性的主要参数为圈闭的充满度和含油饱和度。圈闭含油气性评价隶属于圈闭评价范畴,其研究内容主要包括圈闭地质特征分析、圈闭含油气性主控因素分析、圈闭成藏机理研究及圈闭综合评价4个方面。对于不同盆地、凹陷及不同构造带上的不同圈闭类型,因其勘探程度不同,圈闭含油气性的评价方法也存在差异性,多种评价方法的应用是现今圈闭含油气性评价的客观现实。圈闭评价方法的局限性,给评价圈闭含油气性带来了较大风险,其评价技术还有待深入研究。目前随着隐蔽油气藏勘探的深入,在中国东部形成了一套针对岩性圈闭含油气性评价的方法,并取得了较好的勘探效果,对进一步研究圈闭含油气性具有一定的借鉴意义。     

莺歌海盆地A区新近系地震储层预测及含油气性检测相关岩石物理参数研究

利用 10口井的全波测井资料 ,结合岩石物理测定数据 ,对莺歌海盆地A区新近系地震储层预测及含油气性检测相关岩石物理参数进行了研究 ,指出了在该地区应用常规纵波波阻抗技术进行储层预测及含油气性检测的若干难点。这些研究成果对于在莺歌海盆地合理应用地震储层预测和含油气性检测技术具有实际意义     

用地质力学解释模型预测钻井产油性——在西西伯利亚地区的试用结果

近年来已从评价岩石地质力学状态的角度,研究含油气性问题。同时,在最新的地质动力学范围内(席多洛夫等,1989),以较大的注意力研究地球物理资料的地质动力学解释方法、研究流体过程与不均匀沉积地层中应力分布的关系。现在,我们来讨论近些年在非致密介质地质力学模型范围内地震资料解释方面的一些研究结果。     

基于气相烃类的渤海油田储层含油气性定量评价新方法及其应用效果

新形势下渤海油田储层流体解释难度越来越大,而目前储层含油气性录井评价还处在定性阶段,迫切需要建立录井定量解释模型。通过对大量气相烃类及其衍生参数的特征分析,发现气测烃组分中的重组分与油气指数在油气层段的交会特征与储层含油气性具有相关性,据此拟合出定量评价储层含油气性的新参数——含油气丰度,并建立了基于气相烃类的渤海油田常规储层含油气性定量解释标准。经过多个构造不同层位的油气水层验证,本文方法评价结果与测井测试结论符合度较高,在低对比度等疑难储层的解释方面取得了较好的应用效果,从而为渤海油田储层流体性质的快速评价提供了一种定量化解决思路。     

圈闭评价中含油气性风险依赖性的概念、方法及应用

“至少有一个次级圈闭含有油气”的组合概率分析技术可评价圈闭的含油气性,评价结果反映了所有可能同时含有油气的圈闭组合形式。但受限于客观地质条件约束,有些圈闭是不应该同时出现的--不同圈闭组合形式对资源量计算有直接影响。为了计算符合地质模型约束的圈闭组合概率,界定了次级圈闭的含油气性定量评价模型的地质涵义,指出次级圈闭含油气性评价本质上是遵循贝叶斯分析原则,包括边际概率和条件概率两部分评价内容,分别体现全局成藏和局部成藏的可能性。首先,强调不同圈闭成藏时会存在明显的地质相关性,单个层圈闭的评价是以全局成藏可能性为前提进行评价;其次,通过对应边际概率的可能取值范围,区分了“完全独立”、“部分决定”和“完全决定”3种不同含油气性风险依赖类型,而不同依赖类型直接决定了到底哪些圈闭组合才符合当前地质认识;最后,为油气资源一体化评价软件平台（PetroV）设计了一种改进的概率树分析技术,与不确定性体积法有机结合,实现了“基于含油气性风险依赖的概率组合加和”资源量计算方法。实例证明,要想获得较为客观的圈闭定量评价结果,需要充分考虑其所属不同次级圈闭间的含油气性风险依赖类型,并依此为基础才能给出对应合理地质模型解释的不确定性油气资源量分布结果。     

准噶尔盆地侏罗系储层含油气性相关岩石物理参数

通过模拟地层压力、温度等特征进行岩心测试,结合多波极子测井资料,对准噶尔盆地腹部侏罗系储层地震预测及含油性相关岩石物理参数进行了研究,认为单一地震属性预测岩性存在一些局限性;利用常规地震属性进行含油气检测是不可行的,阐述了在该地区应用常规纵波波阻抗技术进行储层预测及含油气性检测的难点,指出了含油气检测在利用AVO反演的同时,必须联合弹性波反演方法,才能有效提高地震储层预测和含油气性检测的成功率,对准噶尔盆地腹部合理应用地震技术预测储层和含油气性具有实际意义。     

油气储层低频界面极化效应数值模型

基于储层岩石孔隙毛管模型,文中推导了储层宏观物性参数与微观孔隙流体导电性质和介电性质的定量关系,从而构建了一种定量描述储层岩石低频界面极化效应的等效电路模型。相对于传统的Cole-Cole等效电路模型,文中的等效电路模型参数具有更明确的物理含义,更适用于定量表征储层岩石低频界面极化效应。利用该等效电路模型分别对盐水饱和岩样及含油岩样低频界面极化效应进行了数值模拟,分析了储层岩石孔喉比、矿化度及含水饱和度等因素对虚部电阻率频散特征的影响。模拟结果表明,虚部电阻率极小值的模值与孔喉比、矿化度及含水饱和度均呈指数关系,虚部电阻率极小值的模值随孔喉比增大而增大,随矿化度和含水饱和度增大而减小。数值模拟结果为利用岩石低频界面极化效应对储层含油气性进行定量评价提供了理论基础和模型基础。     

应用油田水辅助油气勘探新方法

剖析了塔里木盆地大量油田水样品,对其中溶解烃的浓度及组成与储层含油气性的关系进行了研究,发现对于含油气性不同的储层,其油田水中溶解烃的浓度及内分布不同,干层、水层、气水同层油田水中溶解烃浓度较低,而气层、油层,尤其是含凝析油的气层中油田水样品中烃浓度高;干层、水层、气水同层油田水样品中一般缺少系列碳数烷烃,油水同层、气层和油层油田水中样品中富有系列碳数烷烃,其碳数分布范围为油层≥油水同层气层。依据不同含油气性储层中溶解烃的特征,建立了储层含油气性初步评价模型,可用于辅助油气勘探。     

弹性波阻抗反演技术在辽东湾隐蔽油气藏勘探中的应用

A目标位于辽中凹陷中段中央底辟断裂带西侧,钻井资料证实其所在区域储层岩石物理特征复杂,东营组砂岩和泥岩波阻抗分布范围交错,即使砂岩含油气其波阻抗与围岩的差别也很小,因此应用常规波阻抗反演技术无法解决该区域岩性识别和含油气性检测等问题。根据A目标所在区域的横波速度反演、岩石物理分析以及AVO模型分析结果,利用弹性波阻抗反演技术对该目标进行储层岩性识别和含油气性检测,取得了良好的应用效果,预测结果得到了钻井的证实。     

白家海凸起侏罗系段(J1b1)储集层特征与含油性关系--以彩34井区为例

针对白家海凸起侏罗系J1b1段储层油水同出的情况,有必要开展储集层研究与含油性评价.本文利用岩心样品、测井及试井资料,通过各种分析、测试等手段,论述了研究区八一段碎屑岩的岩石类型、孔隙结构、物性及流体的相渗特征.发现白家海凸起八道湾组八一段存在如下油气藏特征储层的含油性与碎屑岩粒度及岩石物性具有一定的相关性;根据储层物性、微观特征,认为储层物性是影响含油气性的关键;压实作用使储层物性变差,导致含油饱和度不高;本区储层的孔隙结构特性、含油饱和度、相渗特征等决定了八一段储层的产液特征.     

多地质因素的勘探目标优选——人工神经网络法与多元回归分析法比较研究

将人工神经网络法及多元回归分析法分别用于优选预测库车坳陷北带圈闭的勘探目标,结果发现人工神经网络法远比多元回归分析法优越.其根本原因是圈闭的优劣与其相关地质因素之间存在着一个复杂的非线性关系,人工神经网络法所描述的多因素关系恰是非线性的,而多元回归分析法只能描述线性关系.因此,当描述多个地质因素的复杂关系时,应提倡采用人工神经网络法.当然,多元回归分析法也具有人工神经网络所不具备的计算速度快、能较好地表达圈闭优劣与其相关地质因素之间亲疏关系的优点,可作为辅助应用.     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

应用人工神经网络预测油田产量

Ｗｅｎｇ旋回模型是一种常用的石油资源定量评价方法，常被用来预测一个油田的产量，本文应用改进的逆向传播神经网络预测罗马什金油田年产量，与Ｗｗｎｇ旋回模型预测结果比较表明：人工神经网络是一种可行的石油产量外携预测方法。     

人工神经网络在石油工业中的应用及未来发展趋势探讨

在石油勘探、开发和钻井,以及生产过程中,存在着大量且复杂的不确定性因素,所收集和获取的信息不少是非数值型的、不精确的,要靠人工智能的方法加以识别和解决。人工神经网络是在现代神经科学研究成果的基础上提出的一种智能方法,作为一种有效解决非线性问题的网络技术,已在现代石油工业中得到了广泛的应用,并取得了较好的现场应用效果。针对目前人工神经网络的迅速发展及应用情况,论述其在石油工业中的应用现状,并对其在未来石油工业中的发展趋势展开了探讨。最后利用人工神经网络构建了钻井液固相和滤液侵入深度预测模型,分析了该模型的主要特色和实现的技术难点。     

Estimating the initial pressure,permeability and skin factor of oil reservoirs using artificial neural networks

Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems. In this study, a new approach based on artificial neural networks (ANNs) has been designed to estimate the initial pressure, permeability and skin factor of oil reservoir using the pressure build up test data. Five sets of actual field data in conventional and dual porosity reservoirs have been used to test the results of the neural network. The results from the network are in good agreement with the results from Horner plot. Finally, it is shown that the application of artificial neural networks in a pressure build up test reduces the cost of the test and it is also a valuable tool for well testing.     

The Simulation of Microbial Enhanced Oil Recovery by Using a Two-layer Perceptron Neural Network

The authors simulated a reservoir by using two-layer perceptron. Indeed a model was developed to simulate the increase in oil recovery caused by bacteria injection into an oil reservoir. This model was affected by reservoir temperature and amount of water injected into the reservoir for enhancing oil recovery. Comparing experimental and simulation results and also the erratic trend of data show that the neural networks have modeled this system properly. Considering the effects of nonlinear factors and their erratic and unknown impacts on recovered oil, the perceptron neural network can develop a proper model for oil recovery factor in various conditions. The neural networks have not been applied in modeling of microbial enhanced oil recovery since now. Finally, we are going to design a controller for the neural network. This controller is designed for the case where output of the network is oil recovery factor. For this purpose, the network is designed as a one-layer network in which just one output matches each time. In this case, a one-layer network will have acceptable results.     

Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

Predicting the Bubble-Point Pressure and Formation-Volume-Factor of Worldwide Crude Oil Systems

This paper presents models for predicting the bubble-point pressure (P_b) and oil formation-volume-factor at bubble-point (B_ob) for crude oil samples collected from several regions around the world. The regions include major producing oil fields in North and South America, North Sea, South East Asia, Middle East, and Africa. The model was developed using artificial neural networks with 5200 experimentally obtained PVT data sets. This represents the largest data set ever collected to be used in developing P_b and B_ob models. An additional 234 PVT data sets were used to investigate the effectiveness of the neural network models to predict outputs from inputs that were not used during the training process. The network model is able to predict the bubble-point pressure and the oil formation-volume-factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. In order to obtain a generalized accurate model, back propagation with momentum for error minimization was used. The accuracy of the models developed in this study was compared in details with several published correlations. This study shows that if artificial neural networks are successfully trained, they can be excellent reliable predictive tools to estimate crude oil properties better than available correlations. The network models can be easily incorporated into any reservoir simulators and/or production optimization software.     

卷积神经网络在岩性识别中的应用

深度学习是人工智能中的一个重要部分,卷积神经网络作为深度学习一个分支,用多层非线性计算单元可以表达高度非线性和高变度函数。提出将卷积神经网络应用于判别储层岩性的方法,构建了一个双层的卷积神经网络模型,样本回判准确率为99%。通过把卷积神经网络方法与岩石物理相方法和支持向量机方法进行对比,分析卷积神经网络方法准确率高、速度快,岩性预测具有实时性。由此证明卷积神经网络在储层岩性识别中的适用性,且准确率较高。     

单砂体非均质性定量化描述新方法

为了提高渗透率的解释精度 ,选择人工神经网络方法建立了储层渗透率解释模型 ,同时 ,为了能够更快地获得准确、稳定的解释模型 ,对BP网络进行了改进 ,编制了自检验遗传神经网络应用程序 ;然后利用改进的人工神经网络建立渗透率解释模型 ,对储层渗透率剖面进行了逐点解释 ,在此基础上进一步结合分形几何学提出了渗透率的韵律维数、韵律角、韵律高等能够更全面、准确描述储层非均质性的概念。