分频属性反演方法在B油田的应用

针对B油田常规声波阻抗曲线难以识别砂泥岩的储层预测难题,通过多曲线交汇、敏感性分析,确定中子-密度属性反演预测储层的基本思路。在建立三维低频地质模型基础上,对地震资料进行分频处理,利用神经网络方法计算不同厚度下振幅与频率之间的关系,建立测井曲线与地震波形之间的非线性关系,最终摸索出基于精细地质模型的神经网络分频属性反演方法。研究结果表明:该方法解决了复杂储层预测的难题,准确预测了该油田储层的变化规律,为下步开发调整方案编制提供了重要依据。     

地震属性的拟测井反演及在煤层解释中的应用

描述了地震属性的拟测井反演方法,即从地震属性中反演能够反映煤层特征的拟测井曲线。该方法将钻孔处的测井曲线和地震属性作为样本数据,经过神经网络训练后形成网络模型,以此模型反演非钻孔处的地震属性,得到三维测井曲线数据体。该数据体纵向分辨率高,横向分布精确可靠,经过解释,提供高准确度的煤层空间分布情况。      

测井-地震多属性密度曲线重构反演技术及应用

为了提高波阻抗反演的分辨率,在密度资料对反演结果产生重要影响的基础上,利用岩心、测井及地震资料,从岩石物理性质-测井响应-地震响应角度分析,提出应用测井-地震多属性密度曲线重构技术合成拟密度曲线,其结果用全井连续取心样品测试分析数据验证的平均误差为0.05 g/cm3.应用反演效果分析表明,反演结果吻合率从重构前的78%提高到重构后的86%,密度曲线重构反演技术提高了反演精度和分辨率.     

测井与地震资料联合反演预测K2灰岩及其含水性

阳煤五矿主要可采煤层15号煤层顶板发育的K₂灰岩不是良好的地震波反射界面,常规地震剖面很难连续追踪。测井曲线上的K₂灰岩表现为高密度和高视电阻率异常,采用密度与视电阻率两种测井曲线融合生成拟密度曲线,基于模型反演得到地层岩性数据体,从而识别灰岩的赋存形态与厚度;采用概率神经网络反演的方法,优选出9种地震属性,构成神经网络训练样本,对灰岩的孔隙度和视电阻率进行神经网络反演,预测灰岩的富水性。      

油气开发阶段基于Petrel软件的碳酸盐岩储层白云石含量平面预测方法——以川东北C地区应用为例

将地震与测井技术相结合,对已经进入油气开发阶段、有一定测井资料的地区,提出了地震属性约束测井插值预测碳酸盐岩储层白云石体积含量平面分布的方法。方法分三个步骤:(1)利用多矿物模型测井解释技术,计算得到单井白云石含量;(2)利用地震属性与测井白云石含量曲线进行相关分析,找出相关性最好的地震属性;(3)利用Petrel软件的Convergent插值算法,在相关性最好的地震属性约束下,进行井间测井插值。该方法在川东北C地区进行了预测应用,效果较好。准确计算单井白云石含量和优选最相关地震属性,是该方法的关键。     

小波神经网络在测井油水解释中的应用

作者在本文中讨论了小波神经网络概念和油水测井曲线段的频率特性。以小波分析为数据预处理工具，首先用小波分解高频成分对测井曲线进行分段，然后应用小波分析提取出测井段的分频能量特征向量，以此向量代表井段的信息，并作为神经网络的输入，进行神经网络的测井解释，实现了小波神经网络的测井分段油水解释。     

基于粒子群优化算法的多元线性拟合方法研究及其应用

实际测井中,密度曲线最易受扩径的影响。为了消除这种影响,在多元线性拟合方法中引入了粒子群优化算法(Particle Swarm Optimization,PSO),提出了基于粒子群优化算法的多元线性拟合方法,粒子群优化算法是对目标函数优化具有自适应控制的智能进化算法。这里将粒子群优化算法与多元线性拟合法结合,选取井眼环境好,且与扩径层段有相同的岩性、物性的井段作为参考层,利用受井眼环境影响相对小的测井曲线(伽马、电阻率和声波时差),建立更加精确的多元线性拟合模型;再运用这个模型在扩径层段重构密度曲线;最后将重构密度曲线与原始密度曲线、Gardner公式计算的密度曲线进行对比分析。研究结果表明,提出的基于粒子群优化算法的多元线性拟合法重构的密度曲线的合成地震记录与井旁地震道相关系数可达0.86,说明该方法能够更有效提高密度测井曲线的质量。     

利用地震多属性分析技术预测“暗点”型含气储层

充分利用能够反映储层及其含气特征的多种地震属性,结合已知钻井的测井、地质资料进行综合分析,以神经网络技术和属性降维处理为技术手段,估算储层参数及预测储层含气性的方法就是地震多属性分析技术.该技术的关键是储层的精细标定、层位准确追踪,以及沿层提取属性所开时窗的合理性.对孝泉-新场地区须家河组二段气藏的应用实例证实,该技术是运用叠后地震数据对“暗点”型气藏进行储层及其含气性预测的又一有效手段.     

利用地震多属性分析技术预测"暗点"型含气储层

充分利用能够反映储层及其含气特征的多种地震属性,结合已知钻井的测井、地质资料进行综合分析,以神经网络技术和属性降维处理为技术手段,估算储层参数及预测储层含气性的方法就是地震多属性分析技术.该技术的关键是储层的精细标定、层位准确追踪,以及沿层提取属性所开时窗的合理性.对孝泉-新场地区须家河组二段气藏的应用实例证实,该技术是运用叠后地震数据对"暗点"型气藏进行储层及其含气性预测的又一有效手段.     

基于关联规则与随机森林的地震多属性砂体厚度预测

地震属性技术是砂体厚度预测的重要手段,由于目前可从地震数据中提取的地震属性种类较多,在利用地震属性技术前,必须优化出对砂体厚度最敏感的地震属性组合,以减少地震属性信息的重复与冗余。为此提出了一种联合关联规则与随机森林回归算法的地震多属性砂体厚度预测方法。随机森林回归算法能够建立地震多属性与砂体厚度之间的非线性关系,并能进行属性选择,但是该算法无法识别地震多种属性中的冗余特征。关联规则能够发现地震属性之间的非线性关联,并能借助卡方检验消除地震属性间的冗余性。分别采用了随机森林回归算法(RFR)、联合关联规则与随机森林回归(AR-RFR)及BP神经网络回归的算法(AR-BP)对滩坝砂岩合成模型和某实际工区进行了砂体厚度预测。对比结果表明,基于关联规则的属性优选得到的属性间相关性低,关联规则与随机森林算法的结合提高了砂体厚度的预测精度。数值实验证明了该方法的有效性。     

Wavelet Analysis of Geophysical Well-log Data of Bombay Offshore Basin, India

Geophysical well-log (bore-hole) data facilitate understanding of the physical properties of the subsurface formations as a function of depth measured in a well. In the present study, the wavelet transformation technique was applied to the well-log data of three wells in the Bombay High oil field, India, in order to identify depths to the tops of oil and/or gas formation zones (pay zones). Continuous wavelet transformation (CWT) was performed on gamma-ray, resistivity, neutron porosity and velocity log data sets in order to determine the space-localization of the oil and/or gas formation zones. The choice of a mother wavelet is important and largely depends on the data under investigation. We have applied a variety of wavelets to the different log data sets to not only identify the depths to the tops of formation zones, but also to determine the optimum wavelet that best characterizes the pay zones. On examination of scalogram plots of each log corresponding to each wavelet for their better resolution in identifying the formation boundaries, we have found that the scalograms corresponding to the Gaus1 wavelet appeared to give the best resolution in identifying the depths of pay zones in all the well-log data sets of all three wells. To further validate the above observation, a histogram analysis of CWT coefficients is made. This showed that, of all the wavelets considered for the present study, Gaus1 wavelet is the most appropriate and optimum for determining the space-localization of pay zones in all the well-log data sets considered in the present study. The depths of pay zones estimated from scalogram plots of logs agree well with those provided by the Oil and Natural Gas Corporation Ltd., India.     

Stratigraphic correlation of the Devonian sequence in the Blantyre Sub-basin,Darling Basin,western New South Wales∗

Seismic sections and the analysis of lithostratigraphic units from well-log data were used to develop a new stratigraphic correlation of the Winduck, Snake Cave and Ravendale Intervals for the Blantyre Sub-basin. The stratigraphic boundaries of the intervals were defined at marked changes in well-log characteristics, and depth estimates of the boundaries were derived from the well-log data in Mt Emu 1, Blantyre 1 and Kewell East 1. Six seismic-stratigraphic boundaries have been identified in the seismic sections to show the continuity of the latest Silurian to Holocene sediments throughout the Blantyre Sub-basin; from bottom to top they are: H-1, base of the Winduck Interval; H-2, base of the Snake Cave Interval; H-3, base of the Ravendale Interval; H-4/5 base of the undifferentiated Upper Carboniferous/Permian sediments; and H-6 base of the undifferentiated Cenozoic sediments. All stratigraphic boundaries are based on good continuous markers, with strong amplitudes throughout the whole sub-basin. A three-dimensional geological model was developed from the seismic data to map out the geometry of the key reflectors, and hence the structure and stratigraphy of the Winduck, Snake Cave and Ravendale Intervals in the areas where these intervals have been preserved. This model has better defined the Wilcannia High and two smaller highs around the Mt Emu 1 and Snake Flat 1 wells, and further defines the relationships between the stratigraphy, sub-basin geometry and development of complex structures in the Blantyre Sub-basin.     

Geostatistical Seismic Inversion with Direct Sequential Simulation and Co-simulation with Multi-local Distribution Functions

Stochastic sequential simulation is a common modelling technique used in Earth sciences and an integral part of iterative geostatistical seismic inversion methodologies. Traditional stochastic sequential simulation techniques based on bi-point statistics assume, for the entire study area, stationarity of the spatial continuity pattern and a single probability distribution function, as revealed by a single variogram model and inferred from the available experimental data, respectively. In this paper, the traditional direct sequential simulation algorithm is extended to handle non-stationary natural phenomena. The proposed stochastic sequential simulation algorithm can take into consideration multiple regionalized spatial continuity patterns and probability distribution functions, depending on the spatial location of the grid node to be simulated. This work shows the application and discusses the benefits of the proposed stochastic sequential simulation as part of an iterative geostatistical seismic inversion methodology in two distinct geological environments in which non-stationarity behaviour can be assessed by the simultaneous interpretation of the available well-log and seismic reflection data. The results show that the elastic models generated by the proposed stochastic sequential simulation are able to reproduce simultaneously the regional and global variogram models and target distribution functions relative to the average volume of each sub-region. When used as part of a geostatistical seismic inversion procedure, the retrieved inverse models are more geologically realistic, since they incorporate the knowledge of the subsurface geology as provided, for example, by seismic and well-log data interpretation.     

地质统计学反演及其在吉林扶余油田储层预测中的应用

地质统计学反演方法将随机建模技术与常规地震反演相结合,有效地综合地质、测井和三维地震数据,可以更加精确地描述储层的变化.在执行地质统计学反演前,首先应用稀疏脉冲约束反演,了解储层的大致分布,以求取子波和水平变差函数.地质统计学反演从井点出发,井间以原始地震数据作为硬数据,通过随机模拟的产生井间波阻抗,然后将波阻抗转换成反射系数,并用确定性反演方法求得的子波褶积产生地震道,通过反复迭代直至合成地震道与原始地震数据达到一定程度的匹配,反演结果是多个等概率的波阻抗数据体实现.反演结果符合输入数据的地质统计学特征并受地质模型的约束,它综合了测井的垂向分辨率高和地震的横向分辨率高的优势,结果的多个实现用于不确定性评价.     

分形在储层参数井间预测中的应用

现有地震资料难以实现储层薄层的预测，分形理论的引入，可弥补这一不足．以具有连续性和等间距采样点的测井资料为基础，以分形分析理论为手段，结合生产实际进行了研究．根据分形地质统计学中对储层参数的研究，得出其分形理论模型，并通过VisualBasic语言编程，在计算机上实现了某地区4口井储层参数（包括孔隙度、渗透率和泥质含量）的井间预测，其结果达到了预期的效果，说明将分形理论用于储层参数井间预测具有可行性．     

Segmentation of well logs by maximum-likelihood estimation

A maximum-likelihood procedure for segmenting digital well-log data is presented. The method is based on a univariate state variable model in which an observed log is treated as a time-series consisting of two terms: a Gauss-Markov signal remaining constant over a segment, and an additive Gaussian, but not necessarily stationary, noise. The signal jumps by a random amount at a segment boundary. The inverse problem of log segmentation consists of detecting the segment boundaries from a given log. The problem is solved using a Bayesian approach in which the unknown parameters, the locations of segment boundaries and the jumps in the signal value, are estimated by maximizing the likelihood function for the observed data. An algorithm based on Kalman smoothing and single most likelihood replacement (SMLR) procedure is proposed. The performance of the method is illustrated with a case study comprising of multisuite log data from an exploratory well. The method is found to be rapid and robust. The resulting segments are found to be geologically consistent.     

Magnetotelluric soundings for estimating the oil- and-gas content of the Mesozoic complex in Western Kazakhstan

The results of electrical exploration carried out using the method of magnetotelluric sounding at a deposit in Western Kazakhstan are presented. Based on processing and interpretation of the electrometric data, a digital geoelectric model of the exploration area is developed and a geoelectric section of the test profile is constructed. It is shown that MT soundings, along with a seismic survey and well-log data, can be effectively used to define a geological structure and to identify prospective oil-and-gas bearing segments in Mesozoic terrigeneous-sedimentary complexes.     

A heuristic search method for optimal zonation of well logs

Optimal zonation of well-log data, that is, determining an optimal number of major segments such as waveforms in a log, may be achieved by employing a criterion of minimum variance (within a segment) and a heuristic search of potential boundary (link) points of digitized log data. This new method is based on an algorithm originally devised by D. M. Hawkins and D. F. Merriam in 1973. Their method can be improved by introducing a heuristic search procedure, thereby decreasing computer time by 7- to 50-fold, depending on the number of data points and configuration of the logs. Time saving is proportional to the size of the data set. Three examples—one hypothetical and two real—are used to illustrate the modification of the Hawkins and Merriam algorithm.Presented at the Geological Society of America Annual Meeting, Orlando, Florida, 1985.