Empirical approaches in prediction of reservoir sediment distribution——An experience of 57 reservoirs in the USA and India

An accurate prediction of sediment distribution may minimize economic losses through proper and timely planning of the functional activities of a reservoir.This study assesses different temporal and spatial factors that affect for sediment deposition in a reservoir and its distribution.This study also focuses on evaluation of two popular distribution prediction methodologies,Area Increment and Empirical Area Reduction,based on experience with sediment distribution in 57 reservoirs in the USA and India.A non-iterative processed empirical distribution model(NPEDM) and a linear regression trend model(LRTM) are proposed to predict sediment distribution.Silt contributing area and inflow entering a reservoir are found to be the most significant factors affecting in reservoir sediment deposition.Compared to the Empirical Area Reduction method,the Area Increment method provided better prediction.The reservoir classification approach and empirical design distribution type curves given by Borland and Miller(1960) are found to be rational.Shape factor values for different periods indicate that reservoir shape(type) changes with time.Thus,long term prediction is not desirable in Type-Ⅱ Ⅲ reservoirs using the Empirical Area Reduction method.Newly developed the NPEDM shows reasonably good prediction of sediment distribution.The NPEDM is very easy to apply and can be used in any reservoir of any size.Extrapolation of the trend of sediment distribution obtained from the LRTM indicates an accurate short term prediction in a few reservoirs as causes of temporal and spatial variations of sediment distribution including the factors of uncertainties of sediment deposition are implicit within the methodology.     

Application of Multiboost-KELM algorithm to alleviate the collinearity of log curves for evaluating the abundance of organic matter in marine mud shale reservoirs: a case study in Sichuan Basin,China

The total organic carbon (TOC) content reflects the abundance of organic matter in marine mud shale reservoirs and reveals the hydrocarbon potential of the reservoir. Traditional TOC calculation methods based on statistical and machine learning have limited effect in improving the computational accuracy of marine mud shale reservoirs. In this study, the collinearity between log curves of marine mud shale reservoirs was revealed for the first time, which was found to be adverse to the improvement of TOC calculation accuracy. To this end, a new TOC prediction method was proposed based on Multiboost-Kernel extreme learning machine (Multiboost-KELM) bridging geostatistics and machine learning technique. The proposed method not only has good data mining ability, generalization ability and sound adaptivity to small samples, but also has the ability to improve the computational accuracy by reducing the effect of collinearity between logging curves. In prediction of two mud shale reservoirs of Sichuan basin with proposed model, the results showed that the predicted value of TOC was in good consistence with the measured value. The root-mean-square error of TOC predicting results was reduced from 0.415 (back-propagation neural networks) to 0.203 and 1.117 (back-propagation neural networks) to 0.357, respectively; the relative error value decreased by up to 8.9%. The Multiboost-KELM algorithm proposed in this paper can effectively improve the prediction accuracy of TOC in marine mud shale reservoir.     

Evaluation and modification of some empirical and semi-empirical approaches for prediction of area-storage capacity curves in reservoirs of dams

The storage capacity of reservoirs is gradually reduced due to sediment accumulation that causes changes in the area-storage capacity (ASC) curves.Establishing these curves and predicting their future change is an important issue for planners,designers and operators of dams.Many empirical and semiempirical approaches have been suggested for establishing and predicting the future changes for these curves.In this study four empirical and semi-empirical methods were evaluated and three of them were modified to be used for the prediction of changes in the ASC curves due to sedimentation,based on the existing sedimentation survey data for 11 reservoirs in the USK For evaluation,these approaches were reviewed and used to determine sedimentation depth and establishing the ASC curves for the Mosul dam reservoir (MDR),which is the biggest hydraulic structure on the River Tigris in northern Iraq.MDR started operating in 1986 with a storage capacity of 11.11 km3 and a water surface area 380 km2 at normal operation stage (330 m a.s.l.).The results obtained from these methods were evaluated using observed bathymetric survey data that had been collected in 2011 after 25 years of the operation of the dam.The evaluation results showed three methods had presented more accurate results for estimating water depth or sedimentation depth at dam site with percentage error about 1.06-3.30％.Whilst for establishing ASC curves,one method presented good agreement result with survey data.Furthermore,ASC and sedimentation depths at dam site of MDR for periods 50,75,100 and 125 years were estimated using the modified approaches and the area reduction method.The results of the modified methods provided reasonable agreement when compared with the area reduction method proposed by the U.S.Bureau of Reclamation and the agreement became better with an increase in time period.     

Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR) methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for forecasting of monthly total sediment.     

Nonlinear simultaneous inversion of pore structure and physical parameters based on elastic impedance

Carbonate reservoirs have complex pore structures, which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical parameters. The existing rockphysics inversion methods are mainly designed for clastic rocks, and the inversion objects are generally porosity and water saturation. The data used are primarily based on the elastic parameters, and the inversion methods are mainly linear approximations. To date, there has been a lack of a simultaneous pore structure and physical parameter inversion method for carbonate reservoirs. To solve these problems, a new Bayesian nonlinear simultaneous inversion method based on elastic impedance is proposed. This method integrates the differential effective medium model of multiple-porosity rocks, Gassmann equation,Amplitude Versus Offset(AVO) theory, Bayesian theory, and a nonlinear inversion algorithm to achieve the simultaneous quantitative prediction of the pore structure and physical parameters of complex porous reservoirs. The forward modeling indicates that the contribution of the pore structure, i.e., the pore aspect ratio, to the AVO response and elastic impedance is second only to that of porosity and is far greater than that of water saturation. The application to real data shows that the new inversion method for determining the pore structure and physical parameters directly from pre-stack data can accurately predict a reservoir's porosity and water saturation and can evaluate the pore structure of the effective reservoir.     

地震属性优化及其在储层预测中的应用

利用地震资料属性信息预测油气储层已越来越受到石油地球物理工作者的广泛重视。但如何优化地震属性,从而更加精确地预测薄砂岩储层特征,提高其描述精度,更是地质及地球物理勘探家们始终不懈的追求。本文在借鉴主成分分析思想的基础上,提出一种新的地震属性优化方法-约束主成分分析。经理论模型的计算及油田区的实际应用表明:该方法不仅能提高储层预测的精度,而且具有更好的适用性。     

Re-Look to Conventional Techniques for Trapping Efficiency Estimation of a Reservoir

All reservoirs are subjected to sediment inflow and deposition up to a certain extent leading to reduction in their capacity. Thus, the important practical problem related to the life of reservoir is the estimation of sedimentation quantity in the reservoirs. Large number of methods and models are available for estimation of reservoir sedimentation process. However, each model differs greatly in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposit in the reservoir can be determined through the knowledge of its trap efficiency. Trap efficiency （Te） is the proportion of the incoming sediment that is deposited or trapped in a reservoir. Most of the Te estimation methods define a relationship of the T, of the reservoir to their capacity and annual inflow, generally through curves. In this study, the empirical relationships given by Brune and Brown were used and compared for estimating the trap efficiency of Gobindsagar Reservoir （Bhakra Dam） on Satluj River in Bilaspur district of Himachal Pradesh, in the Himalayan region of India. A new set of regression equations has been developed for Brune＇s method and compared with Brown and other available Brune＇s equations. It has been found that Brune＇s equations developed in the present study estimated better than the other Brune＇s equations reported in literature. Later, in the present study it was found that Brown＇s approach was over estimating the T,. Hence it was again modified for Gobindsagar reservoir. It was also identified that sediments coming to this particular reservoir were mainly of coarse nature.     

基于弹性参数加权统计的地震岩相预测方法

岩相信息能够反映储层岩性及流体特征,在地震储层预测中具有重要作用.常规方法主要利用与岩相信息关系密切的弹性参数定性或定量地转化为岩相信息.在实际应用中,弹性参数的获取主要基于叠前地震反演技术.而不同弹性参数的叠前地震反演精度间存在着差异,势必影响岩相的整体预测精度.本文提出对弹性参数进行加权统计来预测岩相.首先,基于贝叶斯理论,引入权重系数来调节弹性参数信息的采用量,构建出最终的目标反演函数;其次,考虑到勘探初期缺少明确的测井岩相信息,提出利用高斯混合分布函数来自动估算岩相先验概率;最后,根据输入弹性参数的取值,计算每类岩相对应的后验概率密度,将目标反演函数取最大后验概率密度时对应的岩相类别作为最终预测的岩相.新方法旨在减少弹性参数精度间的精度差异对岩相预测结果的影响,以期提高地震岩相的预测精度.模型与实际资料测试均表明该方法可行、有效且预测精度较高.     

水库地震最大震级预测初步研究

水库地震最大震级综合影响参数E值预测法及其与最大地震的经验关系自1987年被提出后,在很多大型水库中得到了应用并取得了一定的效果。此后20多年又有一批水库地震被确认。该经验关系是否适用值得研究。文中在收集增补最新水库地震资料的基础上,选取48个水库地震震例资料,统计确定了新的水库地震最大震级与综合影响参数的经验模型,并与已有经验模型进行了比较,最后得出水库地震的最大震级上限≯     

基于逆建模理论的储层特征定量预测方法

定量描述储层特征的物理量称为储层参数,在储层描述工作中储层参数预测是一个重要环节.本文以岩石物理理论为基础研究了利用岩石物理逆建模理论进行储层参数预测的方法.在合理、有效的岩石物理模型的基础上,逆建模方法通过弹性参数的等值面的空间交会可以预测出储层参数.在此基础上提出了一种扩展的岩石物理逆建模方法,在两个不同的储层参数域进行三维岩石物理逆建模,通过逆建模结果的参数域转换可以预测更多的储层参数,为储层特征描述提供了更加充分的数据支持.输入数据的种类对于预测结果的准确性有较大影响,利用本文建立的弹性参数适用性分析方法可以选择出适用性好的弹性参数组合作为输入数据.将本方法应用于模型数据和实际工区数据,取得了较好的预测效果,证明方法具有一定的实用性.     

Establishment and application of logging saturation interpretation equation in vuggy reservoirs

Vuggy reservoirs are the most common, albeit important heterogeneous carbonate reservoirs in China. However, saturation calculations using logging data are not well developed, whereas Archie method is more common. In this study, electrical conduction in a vuggy reservoir is theoretically analyzed to establish a new saturation equation for vuggy reservoirs. We found that vugs have a greater effect on saturation than resistivity, which causes inflection in the rock-electricity curve. Using single-variable experiments, we evaluated the effects of vug size, vug number, and vug distribution on the rock-electricity relation. Based on the general saturation model, a saturation equation for vuggy reservoirs is derived, and the physical significance of the equation parameters is discussed based on the seepage-electricity similarity. The equation parameters depend on the pore structure, and vugs and matrix pore size distribution. Furthermore, a method for calculating the equation parameters is proposed, which uses nuclear magnetic resonance (NMR) data to calculate the capillary pressure curve. Field application of the proposed equation and parameter derivation method shows good match between calculated and experimental results, with an average absolute error of 5.8%.     

煤层气储层物性影响因素和预测方法综述与展望

为了解决煤储层物性的预测方法问题,本文基于大量的文献调研,梳理了煤储层孔隙性和渗透性的影响因素和预测方法,并进行了预测技术展望.研究表明,孔隙性影响因素主要有煤层埋深、压实作用、变质程度和显微组分等,孔隙度预测方法主要有双侧向迭代法、阿尔奇公式裂缝孔隙度估算法、双侧向数值模拟法、相关分析方法及支持向量机等方法;渗透性影响因素主要有煤层埋深、储层压力、煤的变质程度、煤体结构、煤岩组分、应力状态、基质收缩作用和裂隙系统发育程度等,渗透性预测方法主要有F-S计算方法、基于达西定律的计算方法、相关分析法及多层次模糊综合评判法等其他方法.本文认为遵循“地质约束测井、岩心刻度测井”的原则,加强煤层气储层岩石物理研究和物性影响因素分析是基础;常规测井信息与测井新技术信息结合,“多尺度信息融合”建立煤岩孔隙度和渗透率解释新模型,充分发挥多种非线性数学方法的优势构建煤岩物性非线性数学预测方法有一定的实际意义.     

考虑动态克林伯格系数的煤储层渗透率预测模型

随着储层压力的降低,克林伯格效应对渗透率的影响越来越大.现有的煤储层渗透率预测模型大都忽略了克林伯格系数的变化,其预测结果与实际生产存在一定的差异,尤其是在低储层压力阶段.本文以体积不变假设为基础,基于火柴棍模型给出在储层压力降低过程中动态克林伯格系数的计算公式,并建立考虑动态克林伯格系数的渗透率预测模型;深入分析在煤储层压力降低过程中,煤储层渗透率和克林伯格系数的变化规律.研究结果表明：随着储层压力的降低,克林伯格系数呈先增大后减小的变化趋势;在相同储层压力下,克林伯格系数随渗透率增加呈指数减小趋势,随温度增加呈线性增大趋势.本文建立的渗透率模型参数简单易获取,预测结果与实际煤储层渗透率变化规律符合性较好,尤其是在低储层压力阶段,能准确预测煤储层渗透率变化.     

Evaluation of coalbed methane reservoirs from geophysical log data using an improved fuzzy comprehensive decision method and a homologous neural network

The evaluation of coalbed methane reservoirs using log data is an important approach in the exploration and development of coalbed methane reservoirs. Most commonly, regression techniques, fuzzy recognition and neural networks have been used to evaluate coalbed methane reservoirs. It is known that a coalbed is an unusual reservoir. There are many difficulties in using regression methods and empirical qualitative recognition to evaluate a coalbed, but fuzzy recognition, such as the fuzzy comprehensive decision method, and neural networks, such as the back-propagation (BP) network, are widely used. However, there are no effective methods for computing weights for the fuzzy comprehensive decision method, and the BP algorithm is a local optimization algorithm, easily trapped in local minima, which significantly affect the results. In this paper, the recognition method for coal formations is developed; the improved fuzzy comprehensive decision method, which uses an optimization approach for computing weighted coefficients, is developed for the qualitative recognition of coalbed methane reservoirs. The homologous neural network, using a homologous learning algorithm, which is a global search optimization, is presented for the quantitative analysis of parameters for coalbed methane reservoirs. The applied procedures for these methods and some problems related to their application are also discussed. Verification of the above methods is made using log data from the coalbed methane testing area in North China. The effectiveness of the methods is demonstrated by the analysis of results for real log data.     

波阻抗反演在储层预测研究中的应用

随着勘探开发的不断深入, 对油气田的老区勘探开发及二次评价,在储层预测研究上,如何更有效地识别碎屑岩储层及变质岩储层,最大限度地降低波阻抗反演过程中某些不确定性因素造成的影响,以确保储层预测的精度,提高钻探的成功率. 波阻抗反演在以上两种储层反演预测的过程中,能够充分利用钻井、测井、试油、试采等动态、静态资料,更好的对地下地质体进行高精度反演预测,找出油气藏储层在纵向、横向上的分布规律,为井位部署及储量上报提供可靠的依据.     

Numerical analysis of synthetic granulate deposition in a physical model study

The current study focuses on the application of a three-dimensional numerical model for the prediction of morphological bed changes. The sediment deposition in a reservoir during a 10-year-flood was investigated and the results of the simulation were validated with data derived from a physical model study. Because of the small grain sizes in the prototype, synthetic granulate was used in the physical model. The numerical computation domain was a reproduction of the physical model, including the grain sizes and the density of the particles, in order to ensure comparability. The CFD code SSIIM, which solves the RANS-equations in three-dimensions, was used for the simulations. The sediment transport in SSIIM is divided into suspended sediment transport, computed by solving the convection-diffusion equation, and bed-load transport, calculated by an empirical formula. The results of the numerical simulation correspond well to the results of the physical model study. The simulated location and the pattern of the sediment deposition in the reservoir are an accurate representation of the observed distribution in the physical model.     

中国的水库泥沙淤积问题

由于我国有许多河流是含沙最高、输沙量大的多泥沙河流,水库泥沙淤积问题异常严重．主要表现在：淤积数量大,淤积速率快．据统计,截止到1981年底全国水库总淤积量达115×10~8m~3．占统计水库总库容的14．2％．年平均库容损失率达2．3％,高于世界各国．水库的严重淤积,不仅影响水库兴利效益的发挥,严重威胁水库的使用寿命,而且还造成一系列在进行水库规划时未曾充分估计到的环境问题．本文重点从河流水文泥沙特性、我国水库淤积问题的严重性、水库淤积引起的问题及水库防淤减淤措施等4个方面作一较全面的分析和探讨．     

碳酸盐岩地层孔隙度测井环境校正方法与应用

地震储层预测时需要高精度的测井数据,对测井曲线不仅在储层处要进行环境校正,而且在非储层处也要做环境校正。为了消除井眼扩径和其他影响因素对测井曲线的影响,针对声波和密度等孔隙度测井提出了一套校正办法。首先要找出测井曲线的失真,然后针对引起失真的原因,分别针对声波测井和密度测井提出具体校正办法,主要包括基于阵列声波测井的声波时差重新提取和其他资料的重构-编辑法。上述方法在川东北碳酸盐岩区块进行了实验,经分析与对比,证明该方法是有效的,思路和方法对其他区块也有借鉴意义。     

基于地震属性约简的深度学习储层物性参数预测:以莺歌海盆地乐东区为例

储层物性参数作为描述储层特性、储层建模和流体模式的重要指标,其准确估算可以为储层预测提供有力参考依据,但传统储层物性参数反演方法无法兼顾反演精度及空间连续性。针对上述问题,本文引入地震属性作为深度学习算法输入,针对地震属性之间存在的信息冗余特征,利用随机森林-递归消除法对地震属性进行约简预处理,最终建立一种基于地震属性约简的储层物性参数预测方法。实际数据测试结果表明,地震属性约简的深度学习储层物性参数预测结果具有良好的精度及横向分辨率,证实本文方法的有效性。