叙利亚O油田Shiranish组碳酸盐岩储层主控因素分析

利用测井、岩心、薄片、物性分析及压汞资料,对叙利亚O油田上白垩统Shiranish组碳酸盐岩储集层特征及主控因素开展研究,明确生屑滩灰岩和粉晶云岩为研究区主要储层类型,其沉积环境、储集空间、孔渗性能及孔喉结构均不相同。生屑滩灰岩储层以组构选择性孔隙为主要储集空间,具有良好的孔喉结构,储渗性能好,其形成主要受沉积相带及多期溶蚀作用控制;粉晶云岩储集层包括选择性组构孔隙和非选择性组构孔隙2种类型,具有中高孔、高渗特征,其形成主要受大断裂和白云石化作用的影响。     

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.     

PETROPHYSICAL CHARACTERISTICS OF THE MESSINIAN ABU MADI FORMATION IN THE BALTIM EAST AND NORTH FIELDS,OFFSHORE NILE DELTA,EGYPT

Baltim East and North fields in the offshore Nile Delta produce gas‐condensate from accumulations located in the northern portion of the Abu Madi palaeovalley. The hydrocarbons in the Abu Madi Formation are present in sandstone reservoir units referred to as the Level III Main and Level III Lower. In this paper, the petrophysical characteristics of these reservoir units in the Baltim area are described using data from wireline logs (gamma‐ray, density, neutron, sonic and resistivity) from fourteen wells and core data from one well. Results of wireline log and core analyses indicate that the Level III Main can subdivided into two sandstone‐dominated intervals (both interpreted as sandbar deposits) separated by a shale‐rich interval which is a partial barrier to fluid flow. Effective porosity is 9–18.5% and permeability 40–100 mD. Sandstones in the Level III Lower are interpreted as braided channel facies and have effective porosity of 12.5–22% and permeability of 100–500 mD. Isoparametric maps for the Abu Madi Formation sandstone reservoirs based on log and core interpretations show the influence of depositional facies on petrophysical characteristics and can be used to assess possible targets for future exploration and development.     

PERMEABILITY PREDICTION FROM WELL LOGS USING AN IMPROVED WINDOWING TECHNIQUE

Permeability prediction from well logs is of great importance in reservoir characterization and engineering. In this paper, a new method is proposed to correlate conventional well logs and core permeability data. It uses an improved "windowing" technique to incorporate adjacent core data to the permeability predictor in such a way that the scales of the well log and core measurements are matched. It also has the capability to evaluate the reliability of each and every prediction. The method is implemented by the use of a neural network and is demonstrated by means of a case study. The study uses a set of well logs and limited core permeability data to produce continuous permeability profiles. The results show that the permeability profiles are consistent with the core permeability and the geological sequence of the reservoir. The reliability indicator is particularly useful for examining reservoir heterogeneity and sampling.     

应用人工神经网络模型进行油层孔隙度、渗透率预测

本文提出了应用神经网络模型，根据测井曲线进行储层物性参数（孔隙度、渗透率）预测的方法。研究结果表明，这是一种可行的方法。     

Development of artificial neural network models for predicting water saturation and fluid distribution

We have developed artificial neural network (ANN) models to predict water saturation from log data. Two Middle Eastern sandstone reservoirs were investigated. In the first case, an ANN model was tested on the Haradh formation in Oman using wireline logs and core Dean–Stark data. In the second case, the ANN was used to model the saturation–height function in a complex sandstone reservoir.In the first case study, the model is based on a three-layered neural network structure. The model was successfully tested yielding a prediction of water saturation with a root mean square error (RMSE) of around 0.025 (fraction of pore volume P.V.) and a correlation factor of 0.91 to the test data. Furthermore, the ANN model was shown to be superior to conventional statistical methods such as multiple linear regression, which gave a correlation factor of 0.41.In the second case, the model yielded a saturation–height function with an RMSE of 0.079 (fraction P.V.) in saturation when using core porosity and height above free water level. This is a considerable improvement over conventional methods. The error was also greatly reduced when permeability and a lithology indicator were introduced. A minimum error of 0.045 (fraction P.V.) was obtained when using core data such as height, porosity, permeability, lithology and a functional link. We then used gamma ray, neutron, density, resistivity wireline data and the cation exchange capacity as inputs. Our best case which gave an RMSE error of 0.046 (fraction P.V.) was obtained. The ANN was then used to predict the hydrocarbon saturation in the Gharif formation and good results were obtained. The neural network model proved the robustness of saturation prediction in another field for the same formation.     

四川盆地下侏罗统大安寨段沉积微相特征及对储层的控制

下侏罗统大安寨段是四川盆地致密油勘探的重点层位,但油气特征复杂。在对大安寨段的岩性特征进行详细研究时,将大安寨段划分为8种岩相:厚层块状重结晶灰岩相、块状亮晶介壳灰岩相、块状—薄层状泥晶介壳灰岩相、薄层状含泥质介壳灰岩相、泥岩与泥质介壳灰岩的薄互层相、薄层状含介壳泥岩相、黑色泥岩相、紫红色到灰绿色泥岩相。总结了大安寨段的沉积模式,划分为5种沉积微相,即滨浅湖泥、滩后、滩核、滩前和浅湖—半深湖泥。最后,通过对比不同岩性的孔渗特性并结合沉积环境进行分析,明确了沉积微相对储集层的控制作用,滩前微相具有较好的致密油成藏条件。     

DETERMINING HYDRAULIC FLOW UNITS USING A HYBRID NEURAL NETWORK AND MULTI‐RESOLUTION GRAPH‐BASED CLUSTERING METHOD: CASE STUDY FROM SOUTH PARS GASFIELD,IRAN

Hydraulic flow units are defined as reservoir units with lateral continuity whose geological properties controlling fluid flow are consistent and different from those of other flow units. Because pore‐throat size is the ultimate control on fluid flow, each flow unit has a relatively similar pore‐throat size distribution resulting in consistent flow behaviour. The relations between porosity and permeability in terms of hydraulic flow units can be used to characterize heterogeneous carbonate reservoirs. In this study, a quantitative correlation is made between hydraulic flow units and well logs in South Pars gasfield, offshore southern Iran, by integrating intelligent and clustering methods of data analysis. For this purpose, a supervised artificial neural network model was integrated with multi‐resolution graph‐based clustering (MRGC) to identify hydraulic flow units from well log data. The hybrid model provides a more precise definition of flow units compared to definitions based only on a neural network. There is a good agreement between the results of well log analyses and core‐derived flow units. The synthesized flow units derived from the well log data are sufficiently reliable to be considered as inputs in the construction of a 3D reservoir model of the South Pars field.     

用标准测井资料评价克拉玛依油田一西区储集层

克拉玛依油田一西区测井资料分为标准测井和综合测井两大类。大多数井均为标准测井,其资料品质差,所建立的孔、渗模型解释精度不高。针对此问题,提出对于没有综合测井曲线的井,采用神经网络的方法用标准测井曲线反演出其综合测井曲线,据以分岩性建立孔隙度、渗透率模型。用这种方法建立的模型要比直接用标准测井曲线反演的孔隙度、渗透率模型精度高,而且可以满足测井解释的要求。在此基础上,对一西区克拉玛依下亚组油藏进行了储集层质量评价。     

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data(a case study,carbonate Asmari Formation,Zagros Basin,SW Iran)

Assessment of reservoir and fracture parameters is necessary to optimize oil production, especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim. However, due to core limitations, using image log is considered as the best method. This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin, SW Iran, in order to evaluate natural fractures, porosity system, permeability profile and heterogeneity index and accordingly compare the results with core and well data. The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters, when there is no core available for a well. Based on the results from formation micro-imager(FMI) and electrical micro-imager(EMI), Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures. Furthermore, core and image logs indicated that the secondary porosity varies from 0% to 10%. The permeability indicator indicates that zones 3 and 5 have higher permeability index. Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility, mud loss and production index which vary between 1 and 1000 md. In addition,no relationship was observed between core porosity and permeability, while the permeability relied heavily on fracture aperture. Therefore, fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend, due to the high fracture aperture. It can be concluded that the electrical image logs(FMI and EMI) are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin, SW Iran.     

Petroleum reservoir characterization with the aid of artificial neural networks

We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous formations can be predicted with good accuracy using information deduced from readily available geophysical well logs. The methodology by which this is carried out is based on the intelligent and adaptive pattern recognition capabilities of an artificial neural network (three-layer feed forward, back propagation). The need for expensive processes to acquire porosity, permeability and fluid saturation data (such as well testing and extensive coring of the formation) may therefore be greatly reduced. Examples of several neural networks developed during this study are presented.     

Improved Permeability Prediction of a Heterogeneous Carbonate Reservoir Using Artificial Neural Networks Based on the Flow Zone Index Approach

Abstract

Permeability is one of the most important parameters required for reservoir characterization. Although core analysis provides more exact information, core data do not exist for all wells in the reservoir because coring is expensive and time consuming. Therefore, another approach should be sought for permeability determination. The objective of this study was to create an artificial neural network (ANN) model in order to use well log data to predict permeability in uncored wells/intervals. The well log, core, and other data were gathered from an Iranian heterogeneous carbonate reservoir. A flow zone indicator was then predicted using an ANN approach with well logs as input variables. The reservoir was thus classified into different zones based on hydraulic flow units to overcome the extreme heterogeneity. Then, a separate ANN training procedure was followed for each flow zone with log data as input variables and permeability as output. This improved method is capable of permeability prediction in heterogeneous carbonate reservoirs in uncored wells/intervals with an average error of less than 10.9%.     

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酒泉盆地青西坳陷青南凹陷柳沟庄—窟窿山构造下沟组储层岩性主要为低孔、低渗砂砾岩类和泥云岩类，岩石矿物成分复杂、泥质含量高、黄铁矿富集、裂缝类型及组合形式复杂，属典型复杂岩性裂缝—孔隙型储层。在这类复杂岩性裂缝—孔隙型储层中，自然伽马等测井曲线不能很好指示地层中的泥质含量，常规测井资料难以准确识别地层的岩石矿物成分，单条测井曲线与岩心孔隙度之间的关联度低，采用常规的孔隙度测井计算方法存在明显的缺陷，孔隙度计算精度远远不能满足储层评价和储量计算要求。文章利用岩心分析数据和测井信息等资料，采用3层BP神经网络进行学习训练，得到砂砾岩岩类和泥云岩岩类的孔隙度计算模型。利用该模型计算储层孔隙度，其结果与岩心分析孔隙度比较，平均误差小于1.5%，能满足储量计算要求。在实际应用中见到良好效果，孔隙度计算精度明显得到提高。     

致密砂岩储层孔隙度定量预测——以鄂尔多斯盆地姬塬地区长8油层组为例

鄂尔多斯盆地姬塬地区长8油层组为典型的低孔、低渗致密砂岩储层。由于其孔隙结构复杂、非均质性强,应用传统的孔隙度计算方法误差较大,结合姬塬地区长8油层组的具体地质特征,运用广义回归神经网络模型对致密砂岩储层孔隙度进行了预测。结果表明,利用该方法预测的孔隙度与利用岩心分析的孔隙度符合率较高。该方法对于未取心井区致密砂岩储层孔隙度的研究具有很好的应用前景。     

THE IMPACT OF DATA INTEGRATION ON GEOSTATISTICAL POROSITY MODELLING: A CASE STUDY FROM THE BERRI FIELD, SAUDI ARABIA

Understanding the spatial distribution of reservoir properties such as lithology and porosity is essential for development drilling, reserves estimation and fluid flow simulation. However, the data typically come from various sources at various scales and have varying degrees of reliability. Data such as wells logs and cores on their own are generally not adequate to produce an accurate model of a reservoir. Geostatistics provides a means for geologists and engineers to analyze this data, and to transfer the resulting analyses and interpretations for the purpose of reservoir modelling and forecasting. The objective of this paper is to assess the added value that is gained by integrating different types of data (such as depositional facies and seismic impedance) with 3‐D geostatistical porosity models. To achieve this goal, four porosity models of the Hanifa Reservoir at the Berri field (Saudi Arabia) were built using different geostatistical modelling algorithms. The first porosity model was based solely on porosity logs from wells. The other three porosity models were generated using different combinations of porosity logs, depositional facies and seismic impedance data. These models were evaluated qualitatively and quantitatively. The results of this study show that facies‐based porosity models result in the better definition of porosity both vertically and laterally compared to the other models. The seismic‐controlled model was the most precise—seismic data has a greater sample density than well data. Porosity from the wells‐only model has the lowest accuracy compared to the other models, which shows the importance of introducing other types of data in porosity modelling. It is concluded that the utilization of different data sources has a pronounced positive impact when modelling areas with low sampling density. Integrating seismic impedance and facies data in porosity modeling improves the overall model accuracy and generates more reliable images about reservoir heterogeneity.     

沉积相对岩石物理特性从岩心到录井尺度粗化的控制及在石油勘探中的意义

有效的粗化岩石物理特性及跨尺度对比对油藏描述及模拟有极重要的意义。本文用统计数学方法探讨沉积过程和沉积相对油藏岩石物理特性跨尺度对比的影响。对单一沉积过程及渐变水动力所产生的油藏(例如滨岸相)岩心与录井的岩石物理特性可以在较大范围内交叉对比。录井的岩石物理特性能比较实际的反映岩心的特性。对多个沉积过程及突变型水动力所产生的油藏(例如三角洲相)岩心与录井的岩石物理特性交叉对比性差。而且录井的岩石物理特性不能实际的反映岩心的特性尤其是对薄层(<1m)泥沙岩交互相油藏。     

The Application of Committee Machine With Intelligent Systems to the Prediction of Permeability From Petrographic Image Analysis and Well Logs Data: A Case Study From the South Pars Gas Field,South Iran

Abstract

Permeability is the ability of porous rock to transmit fluids. An accurate knowledge of reservoir permeability is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs, and intelligent systems to predict permeability. Petrographic image analysis was employed to measure the optical porosity, pore types, pore morphologies, mineralogy, amount of cement, and type of texture. Available conventional log measurements include bulk density, neutron porosity, and natural gamma ray. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL), and a neuro-fuzzy (NF) model. Afterwards, two types of committee machine with intelligent systems (CMIS) were used to combine the permeability values calculated from the individual intelligent systems: simple averaging and weighted averaging. In the weighted averaging, a genetic algorithm model was employed to obtain the optimal contribution of each expert. The results show that both of the CMIS performed better than NN, FL, and NF models acting alone.     

改进的开窗技术在利用测井资料预测渗透率中的应用

渗透率是油藏描述和油藏工程中一关键性的参数.文中描述了一种计算渗透率的新方法,即在岩心分析化验数据和相关测井曲线数据归一化的基础上,利用改进的开窗技术,借助反馈的神经网络方法逐点计算地层的渗透率.通过在胜坨油田的实际应用,证明该方法预测的渗透率与实际渗透率符合较好,具有推广应用的价值.     

CONTROLS ON RESERVOIR DIAGENESIS IN THE LOWER GORU SANDSTONE FORMATION,LOWER INDUS BASIN,PAKISTAN

Sandstones in the Lower Cretaceous Lower Goru Formation in the Lower Indus Basin, Pakistan, are important reservoir rocks for oil, gas and gas‐condensates. For this study, nine metres of core from depths of more than 3400m from well X‐1 in the north‐central part of the basin were analysed for major variations in porosity and permeability in two Lower Goru sandstone units referred to as the Basal and Massive Sands. The Lower Goru Basal Sand was deposited in lower shoreface to inner shelf settings at the well location, while the Massive Sand was deposited in a middle to lower shoreface setting. In both units, intervals with moderate to good (> 15%) porosities alternate with intervals with very low porosity (<5%), and similar variations in core permeability were observed. In this paper, the reasons for this reservoir quality variation at well X‐1 are investigated. Specifically, the study addresses the influence of different clay types on reservoir porosity and permeability within the Lower Goru sands and the distribution and impact of hard cements such as calcite and quartz. A range of petrographical data is integrated including thin sections, whole rock and clay XRD results and SEM images, which together provide some insights into the causes of reservoir quality variation and into the paragenetic relationships between the authigenic minerals. Chlorite grain coats are present in the higher‐porosity sandstones and are interpreted to have inhibited the formation of quartz overgrowths. Dissolution of feldspar and volcanic rock fragments in both the Basal and Massive Sands has contributed to an increase in overall porosity at well X‐1. Relatively low porosity intervals in the Massive Sand are associated with the absence of chlorite grain coats and the presence of abundant quartz overgrowths. By contrast, low porosity intervals in the Basal Sand have undergone early poikilotopic calcite cementation. The formation of authigenic illite resulted in a significant decrease in permeability in both the Basal and Massive Sands. Chlorite and kaolinite also reduced the permeability. The chlorite originated mainly from the dissolution of volcanic rock fragments or from precursor depositional berthierine clay. The transformation of K‐feldspar to illite is suggested to be the main reaction responsible for the formation of both authigenic illite and quartz overgrowths in the two reservoir units; the observed pressure solution will also have contributed to development of quartz overgrowths.