Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Application of 9-component S-wave 3D seismic data to study sedimentary facies and reservoirs in a biogasbearing area:A case study on the Pleistocene Qigequan Formation in Taidong area,Sanhu Depression,Qaidam Basin,NW China

To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Turbidite Dynamics and Hydrocarbon Reservoir Formation in the Tano Basin: A Coastal West African Perspective

This study examines the turbidite dynamics and hydrocarbon reservoir formation in Ghana’s Tano Basin, which is located in coastal West Africa. Through an exploration of geological processes spanning millions of years, we uncover key factors shaping hydrocarbon accumulation, including source rock richness, temperature, pressure, and geological structures. The research offers valuable insights applicable to exploration, management, and sustainable resource exploitation in coastal West Africa. It facilitates the identification of exploration targets with higher hydrocarbon potential, enables the anticipation of reservoir potential within the Tano Basin, and assists in tailoring exploration and management strategies to specific geological conditions of the Tano Basin. Analysis of fluvial channels sheds light on their impact on landscape formation and hydrocarbon exploration. The investigation into turbidite systems unveils intricate interactions involving tectonics, sea-level fluctuations, and sedimentation patterns, influencing the development of reservoirs. An understanding of sediment transport and depositional settings is essential for efficient reservoir management. Geomorphological features, such as channels, submarine canyons, and distinct channel types, are essential in this situation. A detailed examination of turbidite channel structures, encompassing canyons, channel complexes, convex channels, and U-shaped channels, provides valuable insights and aids in identifying exploration targets like basal lag, channel levees, and lobes. These findings underscore the enduring significance of turbidite systems as conduits for sediment transport, contributing to enhanced reservoir management and efficient hydrocarbon production. The study also highlights how important it is to examine the configuration of sedimentary layers, stacking patterns, and angular laminated facies to identify turbidites, understand reservoir distribution, and improve well design. The dynamic nature of turbidite systems, influenced by basin characteristics such as shape and slope, is highlighted. The research provides valuable insights essential for successful hydrocarbon exploration, reservoir management, and sustainable resource exploitation in coastal West Africa.

Early Description of Reservoir by Using a Single Well Data

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Full field reservoir modeling of shale assets using advanced data-driven analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism（sorption process and flow behavior in complex fracture systems- induced or natural） leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called ＂hard data＂ directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The ＂hard data＂ refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of ＂soft data＂（non-measured, interpretive data such as frac length, width,height and conductivity） in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

An improved data space inversion method to predict reservoir state fields via observed production data

A data-space inversion(DSI)method has been recently proposed and successfully applied to the history matching and production prediction of reservoirs.Based on Bayesian theory,DSI can directly and effectively obtain good posterior flow predictions without inversion of geological parameters of reservoir model.This paper presents an improved DSI method to fast predict reservoir state fields(e.g.saturation and pressure profiles)via observed production data.Firstly,a large number of production curves and state data are generated by reservoir model simulation to expand the data space of original DSI.Then,efficient history matching only on the observed production data is carried out via the original DSI to obtain related parameters which reflects the weight of the real reservoir model relative to prior reservoir models.Finally,those parameters are used to predict the oil saturation and pressure profiles of the real reservoir model by combining large amounts of state data of prior reservoir models.Two examples including conventional heterogeneous and unconventional fractured reservoir are implemented to test the performances of predicting saturation and pressure profiles of this improved DSI method.Besides,this method is also tested in a real field and the obtained results show the high computational efficiency and high accuracy of the practical application of this method.

Application of Integrated Seismic Data Processing and Interpretation to Subtle Reservoir Survey

Nowadays, it becomes very urgent to find remain oil under the oil shortage worldwide.However, most of simple reservoirs have been discovered and those undiscovered are mostly complex structural, stratigraphic and lithologic ones. Summarized in this paper is the integrated seismic processing/interpretation technique established on the basis of pre-stack AVO processing and interpretation.Information feedbacks occurred between the pre-stack and post-stack processes so as to improve the accuracy in utilization of data and avoid pitfalls in seismic attributes. Through the integration of seismic data with geologic data, parameters that were most essential to describing hydrocarbon characteristics were determined and comprehensively appraised, and regularities of reservoir generation and distribution were described so as to accurately appraise reservoirs, delineate favorite traps and pinpoint wells.

Reservoir Multiscale Data Assimilation Using the Ensemble Kalman Filter

In this paper we propose a way to integrate data at different spatial scales using the ensemble Kalman filter (EnKF), such that the finest scale data is sequentially estimated, subject to the available data at the coarse scale (s), as an additional constraint. Relationship between various scales has been modeled via upscaling techniques. The proposed coarse-scale EnKF algorithm is recursive and easily implementable. Our numerical results with the coarse-scale data provide improved fine-scale field estimates when compared to the results with regular EnKF (which did not incorporate the coarse-scale data). We also tested our algorithm with various precisions of the coarse-scale data to account for the inexact relationship between the fine and coarse scale data. As expected, the results show that higher precision in the coarse-scale data, yielded improved estimates.

An Integrated Method of Data Mining and Flow Unit Identification for Typical Low Permeability Reservoir Prediction

With the development of oilfield exploration and mining, the research on continental oil and gas reservoirs has been gradually refined, and the exploration target of offshore reservoir has also entered the hot studystage of small sand bodies, small fault blocks, complex structures, low permeability and various heterogeneous geological bodies. Thus, the marine oil and gas development will inevitably enter thecomplicated reservoir stage;meanwhile the corresponding assessment technologies, engineering measures andexploration method should be designed delicately. Studying on hydraulic flow unit of low permeability reservoir of offshore oilfield has practical significance for connectivity degree and remaining oil distribution. An integrated method which contains the data mining and flow unit identification part was used on the flow unit prediction of low permeability reservoir;the predicted results?were compared with mature commercial system results for verifying its application. This strategy is successfully applied to increase the accuracy by choosing the outstanding prediction result. Excellent computing system could provide more accurate geological information for reservoir characterization.

Meteorological Data Generation for the Numerical Simulation of Stratified Flow in the Joumine Reservoir, Tunisia

Dynamic numerical simulation of water conditions is useful for reservoir management. In remote semi-arid areas, however, meteorological and hydrological time-series data needed for computation are not frequently measured and must be obtained using other information. This paper presents a case study of data generation for the computation of thermal conditions in the Joumine Reservoir, Tunisia. Data from the Wind Finder web site and daily sunshine duration at the nearest weather stations were utilized to generate cloud cover and solar radiation data based on meteorological correlations obtained in Japan, which is located at the same latitude as Tunisia. A time series of inflow water temperature was estimated from air temperature using a numerical filter expressed as a linear second-order differential equation. A numerical simulation using a vertical 2-D （two-dimensional） turbulent flow model for a stratified water body with generated data successfully reproduced seasonal thermal conditions in the reservoir, which were monitored using a thermistor chain.

分支河流体系沉积学工作框架与流程

基于现有的研究成果和存在的问题,探讨了分支河流体系(DFS)研究中的关键科学问题、主要研究内容、研究方法和工作流程。研究结果表明:①DFS研究中最关键的3个科学问题是明确河网结构和河型演变规律、构建沉积标志和沉积模式、分析其形成和分布的控制因素。②DFS研究的主要内容包括建设形态沉积学数据库、现代沉积机理研究、分类研究、建立沉积模式、储层建模与储层预测等5个方面。③DFS研究中的关键技术包括基于遥感图像的形态数据采集、形成机理的水槽和模拟实验、河网重构、顶点位置预测与河道分汊点自动生成方法、储层建模知识库平台等。④DFS研究的基本工作流程是先建立形态沉积学数据库,搭建数据库软件平台,在此基础上选择具有代表性的DFS进行现代沉积解剖,然后综合现代沉积调查、露头解剖和模拟实验成果,形成分类体系,总结各类DFS的识别标志和沉积模式,分层次建立储层预测模型,形成沉积结构储层预测模型的建模软件平台,从而预测沉积体系中有利储层的分布。

九分量横波三维地震在生物气区沉积储层研究中的应用——以柴达木盆地三湖坳陷台东地区更新统七个泉组为例

为解决疏松岩性含气区沉积相恢复及储层预测难题,基于中国首例九分量横波三维地震资料的地震沉积学分析,应用地震地貌学和地震岩性学建立四级等时地层格架,研究柴达木盆地台东地区更新统七个泉组沉积相和储层特征。研究方法和思路:首先,利用九分量横波地震相位旋转、分频融合和地层切片技术,在卫星影像揭示沉积模式的基础上恢复主要标准层沉积相;然后,利用地震属性提取、主因子分析和随机拟合技术计算重点砂体的储层厚度和物性参数,经盲测井检验效果较好。研究结果表明:研究区七个泉组的优势沉积相为三角洲前缘和浅湖,三原色融合切片揭示发育2个旋回,同期存在3套水下分流河道体系;其中七个泉组中下部分流河道砂体厚度大、分布广、物性最好,是研究区有利储层,其渗透率还受成岩作用影响;分流河道砂体储层向涩北一号气田以西延伸范围较广,为该区勘探向西侧外围区扩展提供了依据。

面向水库型小流域的多源水利三维建模方法研究

针对大型河道三维可视化建模中采用激光雷达、声呐等传感器获取水下地形建模方式操作复杂且成本较高的缺陷,在利用基础测绘成果构建实景地形模型的基础上,采用多源三维模型融合方法对月塘水库小流域进行实景建模。首先采用倾斜摄影三维建模技术对重点水利工程进行实景建模,并利用专业建模软件对重要控制单元进行构件级建模;然后利用已有河道断面数据结合河流面状矢量数据,实现流域内胥浦河河道实景三维建模;最后通过人机交互方式对多源实景模型进行微调与相互印证,实现月塘水库小流域多源三维模型精准融合构建。研究成果可为河道型水库的预报调度与洪水风险分析提供可视化模型基础与决策支撑。

非常规油气储层智能压裂技术研究进展与展望

非常规储层油气资源丰富,压裂是释放非常规储层油气的必要手段,但压裂优化是一个多模态、高维度、大尺度、细时空的复杂大系统问题。为实现非常规储层压裂系统开发,梳理了油气压裂人工智能3个应用场景:透明油气藏数智化构建和压裂双甜点智能优选、机理—数据联合驱动的压裂工艺参数智能优化和压裂风险预警及在线监控智能调控,然后明确了智能压裂3个方面技术特征,并展望了智能压裂未来5个方面的技术攻关方向。研究结果表明:(1)形成了地质多源数据、建模迭代更新精准化技术,助力了优选地质工程压裂双甜点;(2)建立了机理数据驱动多目标优化压裂参数方法,促进了裂缝均衡扩展、扩大体积和提高产量;(3)开发了压裂动态监测及在线调控流程,支撑了压裂高效、可持续开发,提高压裂效率和油气采收率;(4)提出了“甜点选择数智化—压裂参数智能化—在线监测调控精准化—四维可视化”的地质—工程“动态一体化”智能压裂新理念。结论认为,强化数字孪生智能压裂可视化技术、构建生成式智能压裂大数据生态系统、融合机理数据的工艺参数优化方法、开发压裂动态智能预测监测技术、创新远程智能压裂决策控制系统,可为智能压裂革新发展提供理论指导和技术支撑,为未来压裂人机交互智能决策和闭环调控的实现指明了方向。

基于集合Kalman滤波的中长期径流预报

为降低中长期径流预报的不确定性,增加水电站水库的发电效益,针对现有方法侧重于提高单一预报模型确定性预报结果的准确性以降低径流预报不确定性的问题,提出一种基于集合Kalman滤波的入库径流确定性预报方法。以旬为预见期的锦西水库实例验证结果表明:相比传统的单一预报模型和传统的信息融合预报模型,基于集合Kalman滤波的中长期径流预报可使RMSE降低4.78 m^(3)/s,合格率可提高0.56%,且更有效地降低了汛期预报的不确定性,得到了更加准确、可靠的确定性径流预报结果,可为开展流域梯级水电站优化调度提供技术支持。

大庆SN油田东部过渡带油水边界综合确定

为确定大庆SN油田东部过渡带油水界面,综合钻井、测井、地震资料,结合岩心含油产状分析和老井油水层二次解释,基于双相介质理论的叠后属性油气检测以及基于叠前地震波形指示反演的流体识别等技术,探讨构造油气藏外扩区油水边界的综合确定方法。研究区油水边界具有以下特征:岩心含油产状为油斑以上;测井解释外推为油层或油水同层;叠后属性低高频能量比大于0.85;叠前反演预测含水饱和度小于75%。因此,以“井点定深度、地震定边界、动态来验证”为原则,从“点—线—面—空”,经过综合分析,确定最终油水边界位置。研究成果有效指导了研究区外扩评价部署,对同类型构造油田油水边界研究具有参考意义。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

基于压缩感知的波形指示反演在薄储层预测中的应用

鄂尔多斯盆地东缘大牛地气田二叠系下石盒子组有效砂体致密、规模小、厚度薄,储层非均质性强且地震波阻抗叠置严重。为实现下石盒子组5~6 m致密薄砂岩储层的有效刻画,文中选取了盒1段气藏主力单砂体作为研究对象。在地震正演分析基础上,形成了叠前道集优化提质+压缩感知拓频+纵波标定+波形指示GR反演的技术组合,该技术组合将压缩感知技术与波形指示反演技术的优势有机结合起来,并在盒1段气藏主力单砂体进行了应用。预测结果表明:地震剖面横、纵向分辨率显著提高,其预测成果更加符合地质沉积规律,与实钻井吻合良好,实现了5~6 m致密薄砂岩的有效刻画。该研究为致密砂岩气藏薄储层预测提供了一种新方法。