The Design of New Soft Sensors Based on PCA and a Neural Network for Parameters Estimation of a Petroleum Reservoir

Abstract

A new set of soft sensors is presented based on principal component analysis (PCA) and artificial neural network (ANN) methodologies for parameters estimation of a petroleum reservoir. The crude diagrams of reservoir parameters provide valuable evaluation for petrophysical parameters. These parameters, however, are usually difficult to measure due to limitations on cost reliability considerations, inappropriate instrument maintenance, and sensor failures. PCA is utilized to develop new soft sensors to incorporate reliability and prediction capabilities of ANN. For this purpose, a PCA model is derived to reconstruct a parameter from other reservoir parameters using their redundancy relations. The developed soft sensors are applied to reconstruct parameters of Marun reservoir located in Ahwaz, Iran, by utilizing the available geophysical well log data. The experimental results demonstrate that the proposed hybrid PCA-NN algorithm is able to reveal a better performance than the PCA and the conventional back propagation–based NNs.     

特低渗砂砾岩储层的测井评价

特低渗砂砾岩储层非均质性强,油水层判别困难。针对此,提出了采用测井相分析技术以及基于岩石物理相类型建立测井参数评价模型的储层判别方法。方法的思路是,基于测井相分析进行储层岩石物理相划分,使同类岩石物理相具有相似的岩石学特征,孔-渗关系呈规律性变化,表现出相似的岩电关系和测井响应特征;针对不同岩石物理相类型建立储层参数解释模型,真正考虑储层在岩性和物性上的变化,将地层的非均质性问题转变为均质性问题;选取与油水层关系密切的测井参数或计算参数,采用主成分分析等数学方法,提取反映油水层特征的综合特征参数,进行油水层判别。对方法进行了软件开发,并应用于研究区15口井的测井资料处理。结果表明,该方法有效地提高了储层参数解释精度和油水层判别准确度。     

岩石物理参数的流体敏感性分析

岩石物理参数分析是储层流体检测和岩性识别的基础,针对不同岩石物理参数对储层流体的敏感性各异,提出了岩石物理流体敏感性参数的构建方法,并对不同的岩石物理参数及组合进行流体敏感性的分析。在岩石物理参数分类基础上,构建由剪性参数和体性参数组成的组合参数,将体性参数入表示为干岩石参量和流体参量两部分的综合贡献,参考实验数据,选择最佳系数c值,使构建的组合参数最大程度地反映流体的贡献。在东营北带,通过实验室中深层岩石样品的测定和计算,得到基本岩石物理参数,按照流体敏感参数的定义对该区的不同岩石物理参数及组合进行流体敏感性分析,结果表明,构建的组合参数对储层流体的敏感程度要高得多,这对寻求敏感性较好的岩石物理参数进行储层流体识别具有指示意义。     

Intelligent Prediction of Porosity and Permeability from Well Logs for An Iranian Fractured Carbonate Reservoir

Abstract

One of the most important processes in reservoir engineering is reservoir characterization, in which the reservoir parameters such as porosity and permeability are calculated. These parameters have crucial importance in reservoir engineering computations like reserve estimates and reservoir management. Estimation of porosity and permeability from conventional well logs for uncored well intervals is a good suggestion, but the complexity of the fractured carbonate reservoir makes the application of traditional statistical models totally unreliable. In this article the power of the pattern recognition of artificial neural networks (ANNs) has been applied to develop a transformation map from available most related well logs to rock petrophysical properties of Darquvain reservoir in the southwest of Iran. Comparison of the obtained results illustrates that ANN models can yield more reliable results with respect to traditional models of estimating petrophysical properties. An ANN can be utilized as a flexible and powerful tool for reservoir characterization from available well logs in development projects in the oil and gas industry.     

低孔低渗储集层岩石物理分类方法的讨论

为了在储集层孔隙结构和岩石物理特征基本相同的情况下建立测井解释岩电参数模型，需要按岩石物理特征对储集层进行分类。通过实际资料和理论分析，对比地层流动带指数与储集层品质指数两种宏观物性参数的差异，研究储集层岩石物理分类的有效方法和反映微观孔隙结构变化的特征参数。利用两种指数对松辽盆地大情字井地区和鄂尔多斯盆地姬塬地区典型低孔低渗储集层60块岩心的压汞实验资料进行了分类，结果表明，按照储集层品质指数对储集层进行分类能更准确地反映储集层的孔隙结构和岩石物理特征。理论分析亦证明，储集层品质指数与孔隙结构之间呈单调函数关系，而地层流动带指数与储集层孔隙结构之间并不是简单的单调函数关系，储集层品质指数比地层流动带指数能更准确地反映储集层孔隙结构和岩石物理性质的变化。图6表1参14     

The Estimation of Formation Permeability in a Carbonate Reservoir Using an Artificial Neural Network

Abstract

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods need to be developed to predict permeability from well logs. One of the most powerful tools that we applied by the authors is artificial neural network (ANN), whose advantages and disadvantages have been discussed by several authors. In particular, 767 data sets were used from five wells of Bangestan reservoir in a southwestern field of Iran. Depth, Neutron (NPHI), Density (RHOB), Sonic (DT) logs, and evaluated total porosity (PHIT) from log data were used as the input data and horizontal permeability obtained by coring was as target data. Sixty percent of these data points were used for training and the remaining for predicting the permeability (i.e., validation and testing). An appropriate ANN was developed and a correlation coefficient (R) of 0.965 was obtained by comparing permeability predictions and the actual measurements. As a result, the neural science can be used effectively to estimate formation permeability from well log data.     

Integration of seismic attributes and production data for infill drilling strategies — A virtual intelligence approach

Field development strategies are at the forefront of common engineering practices in the oil and gas industry. Reservoir simulation is the most commonly applied methodology to generate an optimum field development plan. However, reservoir simulation can be an energy and cost intensive method that often relies on rather subjective assumption of input parameters, due to lack of accurate field data. In this paper, a new approach using Artificial Neural Network (ANN) technology is proposed to predict individual well performances and accordingly develop infill drilling strategies. Due to its predictive capabilities, ANN is used as a tool to construct a correlation for production prediction. Seismic attributes, which capture heterogeneity of the reservoir geology, and completion information are used as network inputs. In calculating the interference effects, the geometry of the flow system under consideration was used together with the geometric location and the starting production schedule of each well within the system. The method was successfully implemented on a case study of the 19N 94W Township of the Wamsutter field in Wyoming using actual seismic attributes, completion information, well configuration, and production data. Production predictions were generated by the network for all locations at which seismic attributes were available. More promising locations were then selected for infill drilling purposes based on predicted productions at these locations. The predicted initial rate and 10-yr cumulative production were considered in the selection of infill drilling locations with high productivity potential. Results from this work show that the ANN was able to map the relationship between production, completion information, interference effects, and reservoir characteristics captured in seismic attributes. The proposed methodology allowed the construction of spatial maps of gas production, revealing new sweet spots which could not be identified from the existing production history alone. The production maps derived from the ANN predictions contain important heterogeneous features associated with reservoir properties reflected in seismic data. Even though well interference was initially thought to have a limited effect on well performance for the case study presented, the incorporation of well interference parameters in the network design improved production predictions, suggesting that well interference has a more significant impact on well performance than originally anticipated.     

DH油田1号背斜油藏描述

本文综合应用DH油田的地震、钻井、测井和物性分析化验资料, 描述了DH1号背斜石炭系油藏形态和储层的地球物理特征。应用合成声波测井技术, 从井资料出发, 利用地震层速度信息, 预测D砂岩含油段主砂体的分布, 预测油层段孔隙率、含油饱和度等物性参数的横向变化, 计算该油藏的石油地质储量。     

砂岩油藏地震数据流体替换

流体替换常用于计算油藏流体变化前后岩石弹性参数变化,是进行地震油藏描述与监测研究的关键步骤。流体替换需要孔隙度、岩石基质、流体属性和泥质含量等数据信息,因此目前往往只能在有测井数据的油藏部分进行流体替换,并通过地震数值模拟获取流体变化前后地震响应,从而进行地震属性分析与优化。为了扩大流体替换应用范围,提出利用地震资料直接进行流体替换。在确定油藏流体类型与岩石物理模型基础上,建立油藏地质模型,模拟油藏流体变化前后地震反射振幅峰值关系,并分析储层参数（包括孔隙度、储层厚度和泥质含量等）变化时该振幅关系变化特征。采用曲线拟合方式对该振幅峰值关系进行计算,得到可直接应用于地震数据的流体替换线性拟合方程,从而进行实际地震数据流体替换。基于胶结砂岩油藏岩石物理模型的模拟分析与实际资料应用试验,证明了基于地震数据流体替换的可行性。     

Exact Lithologic Boundary Detection Based on Wavelet Transform Analysis and Real-Time Investigation of Facies Discontinuities Using Drilling Data

Abstract

Exact detection of lithologic boundaries is one of the main challenges in exploration, drilling operations, and geology. Investigation of facies discontinuities has been performed using petrophysical data regarding sharp changes along the wellbore. Due to the fact that recorded well logging signals contain lots of high-frequency waves (noise), detection of the layer boundaries comes with some uncertainties that should be eliminated by denoising those signals. Wavelet transform analysis is a good approach to denoise the signals and its ability has been proven in several studies. In this study, implementation of wavelet transform analysis resulted in an innovative approach for exact differentiation of neighborhood lithologic units.

Detection of boundaries between different layers, especially the ones in the vicinity of the reservoir during drilling operations, is one of the crucial issues in petroleum well engineering. This purpose is usually achieved by cutting analysis and geological maps, which are not accurate enough and may cause substantial problems. Unconfined rock compressive strength can be considered as an accurate criterion to detect geological boundaries. In this study, an artificial neural network (ANN) model is developed that can predict the unconfined rock compressive strength of formations being drilled by importing 10 drilling parameters as inputs. Because rock strength will experience sudden changes while entering the next layer, it can be used as a key parameter to determine boundaries.     

利用储层物性参数预测油气

储层物性参数处理技术是根据双相介质理论,使用地震、地质数据提取储层的孔隙度、密度、层速度、视渗透率、流体密度等物性参数的一种处理方法.这些物性参数剖面比常规地震剖面更细致地反映了地下各种地质现象,揭示地质体的岩性变化及油气聚集规律.多年来该技术已在海上、陆上许多油田使用,并得到完善和发展.文中重点介绍在南海北部不同沉积盆地应用该技术进行油气预测的实例及认识.     

基于布谷鸟算法的储层物性参数同步反演

物性反演是储层预测与评价的重要手段,可直观描述储集层所蕴含的信息。由于地球物理反演的非线性特征,以局部寻优方法开展的储层物性参数反演方法难以降低不适定性,结果存在多解性。为此,提出一种基于布谷鸟算法的储层物性参数同步反演方法。以弹性阻抗与储层物性参数关系为基础,构建储层物性反演目标函数,引入布谷鸟算法寻找目标函数最优解。布谷鸟算法作为一种新型元启发式算法,其包含的Levy飞行机制能有效解决常规方法陷入局部极值的问题,可实现更高精度的储层物性参数预测。理论模型和实际数据测试均表明,该方法能够有效反演物性参数,可为储层描述提供数据支持。     

东濮凹陷深层气测井评价及应用

东濮凹陷深层具有比较丰富的油气资源,但由于埋藏深,储层物性差,在测井资料上气层显示特征微弱.在分析深层气的测井微弱显示特征基础上,通过岩心刻度测井技术,建立深层储层参数解释模型,形成了一套利用孔隙度、渗透率、含油气饱和度和含气指数等参数综合判别深层气的方法.在实际资料处理解释和应用中,该方法见到了良好的地质应用效果.     

MCSEM和地震AVA数据联合反演储层物性

海洋可控源电磁（MCSEM）数据对高阻的含油气储层比较敏感,联合反演MCSEM和地震数据可提高储层物性参数估计的可靠性。本文提出一种利用MCSEM和地震AVA数据联合反演储层物性参数的方法,该方法利用Archie公式和Gassmann方程建立储层物性参数与电磁属性（电导率）和地震属性（速度、密度）之间的联系,采用模拟退火（SA）算法求解联合反演目标函数。模型试验表明,与单一数据反演结果相比,联合反演MCSEM数据和地震AVA数据能够得到更可靠的储层物性参数估计,且与基于梯度下降的Occam反演方法相比,该方法不依赖于初始值。     

时移地震AVO反演在油藏定量解释中的应用

时移地震叠前资料包含重要的振幅随偏移距变化(AVO)信息,能用于区分不同油藏参数变化及进行油藏定量解释。依据油田实际情况,确定适合计算疏松砂岩纵、横波速度和密度的岩石物理模型,并依据该模型通过求解精确Zoeppritz方程来正演模拟油藏含油饱和度与有效压力变化时的地震AVO响应,分析了利用时移地震AVO反演区分油藏参数变化、实现油藏定量解释的可行性。根据Aki和Richards的简化AVO方程,详细推导了利用岩石物性与地震属性的近似关系进行时移地震AVO反演的计算公式。试验结果表明,在S油田进行时移地震AVO反演是可行的,推导的时移地震AVO反演计算方法能有效地分离油藏含油饱和度和有效压力的变化,真正实现对油藏的定量解释。     

准噶尔盆地玛北地区三叠系百口泉组岩石物理相

准噶尔盆地玛北地区三叠系百口泉组储层非均质性较强,多因素综合分析成为近年来精细储层研究的重要手段.通过岩心、铸体薄片、测井曲线、粒度等资料,开展综合储层沉积微相、成岩相和裂缝相特征的岩石物理相研究,并对所划分的岩石物理相进行聚类分析,开展对研究区有利储层的预测.结果表明:①扇三角洲沉积微相主要发育分流河道、辫状分支水道...     

用测井资料预测探井碎屑岩储层岩性、物性及敏感性

应用测井资料确定储层岩性,物性是个基本问题,但确定敏感性是一个全新的课题。在收集薄片,铸体薄片,粒度,压汞、Ｘ－衍射,扫描电镜,物性,敏感性流动实验等各种岩心分析资料的基础上,首次探讨了如何应用测井资料确定储层敏感性的问题。从测井资料中提取了１４个央 ,物性参数。     

注水开发后期下二门油田储层特征

二门油田经过近20年的注水开发,目前已进入高含水后期开采,由于该油田储层矿物及胶结物组分成熟度低,不稳定矿物和易溶解胶结物含量高,因此注入水的长期冲刷对储层物性及其它渗流特征参数影响较大,主要表现在：开发后期储层类型增多;孔隙和喉道的分布及组合配置关系更加多样化;大孔（喉）道与小孔（喉）道出现的频率和变化幅度在微观上表现出极大的差异性;相对渗透率参数变化范围加大且曲线类型更加复杂;油层润湿性、驱油效率及剩余油分布特征也发生相应的变化,使得储层非均质性更为严重,开采难度加大,常规“控水稳油””措施受阻。成果采用对比井分析、室内岩心物模实验、测井解释、动态分析、电子探针、敏感性评价等技术对注水开发后期储层特征进行详细探讨,其研究思路、方法和技术可在中国东部砂砾岩油藏储层评价中推广使用。     

靖安油田长6储层敏感性参数与测井方法的相关性分析

在岩心分析资料的基础上,分析和提取了靖安油田长6储层的12个主要敏感性参数,利用常规测井方法可获得岩石骨架,泥质含量,粘土矿物,物性等敏感性参数,然后在油田储层敏感性实验资料的基础上,建立储层敏感性与测井解释参数之间的相关关系,用逐步回归方法确定其解释模型,结合油田实际的储层敏感性评价标准,对实际井进行处理分析,用测井方法预测储层的敏感性,结果表明同测井解释得到的储层敏感性结论与研究区的敏感性流动实验结果基本一致。