A Fuzzy Logic Model for Predicting Dipole Shear Sonic Imager Parameters From Conventional Well Logs

Sonic wave velocities, including compressional, shear, and Stoneley, are functional and practical parameters for various branches of reservoir characterization. In the last few years, several tools such as the dipole shear sonic imager (DSI) were introduced for measuring sonic wave velocities. Most of these instruments are expensive and drilling companies do not run them in all wells of a field because of economical restrictions. In this study, an accurate, intelligent, and indirect method was presented for prediction of sonic wave velocities, which are directly obtained from dipole shear sonic log, utilizing conventional well log data and fuzzy logic technique. Furthermore, the proposed methodology was applied on a carbonate reservoir of Iran. The results of the case study demonstrated the capabilities of fuzzy logic for estimation of sonic wave velocities, where DSI may not be run. The MSEs of the predicted V_p, V_s , and V_st in the test data calculated 1.47 (US/F), 4.96 (US/F), and 2.219 (US/F), respectively, which correspond to the R ² values of 91.8%, 89.3%, and 90.4%, respectively.     

Body Wave Velocities Estimation From Wireline Log Data Utilizing an Artificial Neural Network for a Carbonate Reservoir,South Iran

Reservoir characterization is a prerequisite study for oil and gas field development. Body wave velocities are important parameters for reservoir characterization studies. In this research, a back-propagation artificial neural network (BP-ANN) including the Levenberg-Marquardt training algorithm was used as an intelligent tool to estimate compressional and shear wave velocities. The efficiency of utilizing density log and photoelectric effect (PEF) in improving estimation accuracy have been evaluated as well. The petrophysical data from three wells were used for constructing intelligent models in the South Pars field, Southern Iran. The fourth and fifth wells from the field were used to evaluate the reliability of the model. The results showed that a BP-ANN was successful in estimating body wave velocities and so when just gamma ray, neutron, deep resistivity (lateral log deep) were used as net work inputs, the net exactness ware comparatively low but using PEF effects increased this exactness. By using density log the net exactness noticeably grew and in this manner using both PEF and density log beside other mentioned logs as inputs approached to more real results.     

Experiments on acoustic measurement of fractured rocks and application of acoustic logging data to evaluation of fractures

Fractures in oil and gas reservoirs have been the topic of many studies and have attracted reservoir research all over the world. Because of the complexities of the fractures, it is difficult to use fractured reservoir core samples to investigate true underground conditions. Due to the diversity of the fracture parameters, the simulation and evaluation of fractured rock in the laboratory setting is also difficult. Previous researchers have typically used a single material, such as resin, to simulate fractures. There has been a great deal of simplifying of the materials and conditions, which has led to disappointing results in application. In the present study, sandstone core samples were selected and sectioned to simulate fractures, and the changes of the compressional and shear waves were measured with the gradual increasing of the fracture width. The effects of the simulated fracture width on the acoustic wave velocity and amplitude were analyzed. Two variables were defined: H represents the amplitude attenuation ratio of the compressional and shear wave, and x represents the transit time difference value of the shear wave and compressional wave divided by the transit time of the compressional wave. The effect of fracture width on these two physical quantities was then analyzed. Finally, the methods of quantitative evaluation for fracture width with H and x were obtained. The experimental results showed that the rock fractures linearly reduced the velocity of the shear and compressional waves. The effect of twin fractures on the compressional velocity was almost equal to that of a single fracture which had the same fracture width as the sum of the twin fractures. At the same time, the existence of fractures led to acoustic wave amplitude attenuations, and the compressional wave attenuation was two times greater than that of the shear wave. In this paper, a method was proposed to calculate the fracture width with x and H, then this was applied to the array acoustic imaging logging data. The application examples showed that the calculated fracture width could be compared with fractures on the electric imaging logs. These rules were applied in the well logs to effectively evaluate the fractures, under the case of no image logs, which had significance to prospecting and development of oil and gas in fractured reservoirs.     

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%.     

Averaging Effect on Elastic Wave Velocity in an Integrated Flow Model

Abstract

This article discusses the effect of averaging seismic attributes resulting from a petrophysical algorithm which is used in association with a traditional fluid flow simulator to form an integrated flow model (IFM). Few attempts have been made to evaluate an IFM called GEOFLO by comparison with experimental models. Arithmetic averaging was used to get a single value for a calculated parameter in each grid that is used for comparison with an experimentally measured average parameter, yet different averaging techniques have not been discussed. Therefore, in this research, four different averaging techniques were used to investigate the effect of reducing many values into a single representative value. The methods considered include arithmetic, harmonic, geometric, and root-mean-square averaging techniques. Comparisons were made for compressional and shear wave velocities as well as velocity ratios using core samples of different lithologies. Results show overlapped data from all averaging techniques used, which implies very little effect of the averaging process of the elastic wave velocities using the petrophysical algorithm. Therefore any averaging technique can be used for a one-dimensional reservoir divided into different grids, and that most likely it will yield an average calculated value that would be comparable to the measured elastic wave velocity.     

Predicting oil saturation from velocities using petrophysical models and artificial neural networks

The degree of oil saturation has been estimated from velocity measurements of unconsolidated sediments at a laboratory scale using a petrophysical model and artificial neural network (ANN) as an inversion tool. Laboratory measurements of velocities, V_p, V_s and their ratio V_p/V_s as well as the oil saturation levels of unconsolidated materials from an oil field were performed and the data were analyzed. It was observed that the ratio V_p/V_s increase with an increase in temperature for all saturation level. Beyond a critical saturation level (S_oil=40%), V_p increases with an increase in temperature while V_p/V_s decreases with an increase in temperature. An ANN is trained with simulated data based on the petrophysical model. The weighting coefficients developed from the training are then used to invert for the unknown oil saturation level given the laboratory measured velocities. Simultaneous use of V_p, V_s and V_p/V_s as input variables to the network in training the network give more accurate predictions than when say, V_p or V_s is used individually as input attribute in the inversion process. The results show a good match between the predicted and the measured degree of oil saturation.     

多极子阵列声波测井资料处理及应用

介绍了胜利石油管理局测井公司新研制成功的多极子阵列声波测井仪器采集的数据结构类型.研究了多极子阵列声波测井资料的处理方法.用矩阵、相位等处理方法得到地层纵波、横波、斯通利波时差,利用偶极资料进行快慢横波分离,有效计算地层横波速度各向异性.胜利油田3口井的实际应用表明多极子阵列声波测井资料与其他测井资料相结合,可进行油藏评价,如井眼稳定性分析、地层孔隙压力预测、压裂施工压力设计及缝高检测等.实际钻井资料证明多极子阵列声波测井资料在油田勘探、开发中能够发挥重要作用.     

多极子阵列声波(MAC)在油田生产中的应用

MAC-多极子阵列声波技术是目前较先进的测井技术。高质量的数字阵列声波提供纵波、横波和斯通利渡时差具有十分重要的作用，通过综合处理，可以得到各种岩石强度参数和岩石的破裂压力等，为油层的改造和油田的生产提供准确的参数。     

阵列声波测井资料在吐哈油田致密砂岩气层识别中的应用

吐哈盆地致密砂岩储层具有孔隙度低、渗透性差、孔隙结构复杂等特点,利用常规测井资料难以对储层含气性进行有效评价。以研究区致密砂岩声学特征实验为基础,分析岩样在饱含气状态、饱和水状态下纵波速度和横波速度等参数的变化规律,实验为利用阵列声波测井资料所提取纵、横波时差和常规测井资料计算地层的泊松比、体积压缩系数等参数进行气层识别提供了理论基础。通过储层岩心声学物理试验研究证明,地层在高含气饱和度情况下体积压缩系数和泊松比对含气饱和度变化较敏感;根据阵列声波测井资料计算得到的泊松比曲线和体积压缩系数曲线交会可以直观识别气层。给出的解释实例证实了在吐哈盆地致密砂岩储层利用泊松比与体积压缩系数交会法识别气层的实用性。     

A compressional wave seismic model for the Ashrafi-1 well

Despite disappointing drilling results, the South Caspian basin has yielded scientific results of significant value to future exploration. This paper describes work that utilized data from the offshore Ashrafi-1 well, but we believe it has applications to exploration elsewhere in Azerbaijan. The North Absheron Operating Company (NAOC) drilled the Ashrafi-1 well between November 30, 1997 and February 6, 1998 to a total depth of 3668 m (sub Caspian sea level). The Balakhany and Pereryva Suite (SP) Formations were wet but the Upper Kirmaku Sandy (NKP) Formation tested oil and the Lower Kirmaku (PK) Formation tested gas, both at high flow rates. Anomalously high stack amplitude responses, generally conforming to structural closure, had been observed in the 3D seismic data at the NKP and PK Formations prior to drilling of the well. An offset synthetic modeling study was undertaken, post-drill, to determine the in-situ amplitude response (stack and amplitude variation with offset, or AVO) for both pay and wet zones and to model the changes in AVO and stacked amplitudes in response to changes in variables such as thickness, fluid fill and porosity. As well as the 3D seismic data set, the principal data sources used were a dipole sonic log for compressional wave velocity (Vp) and shear wave velocity (Vs); checkshots for time-to depth (T–D) conversion; a density log; log interpretations of porosity; fluid properties based on pressure–volume–temperature (PVT) analysis of fluids from drill stem tests (DSTs) and pressure/temperature data. In this simple case, despite the highly permeable reservoir rocks, the Gassmann assumption of immobile fluids within the pore space was used. Although the effects of partial gas saturation are known to be a problem elsewhere in this basin, density modeling derived from borehole-guided long offset AVO was not attempted. Despite this, normal incident amplitudes, derived from pre-stack data, cross-plotted against amplitude gradients suggest a methodology for distinguishing wet sandstones from gas and oil in this basin in the absence of recent leakage. Despite the difference in response for oil and gas (oil gave a stronger response than gas), a statistically meaningful number of wells is not yet available and this result may be influenced by local lithologic effects. Thus, the ability to distinguish oil from gas ahead of the drill bit — which is of vital importance in evaluating commerciality — is not proven.     

基于流动单元的碳酸盐岩渗透率建模方法

在储集类型复杂的碳酸盐岩储层中，由于其强烈的非均质的影响，常规的利用孔隙度来评价储层渗透率的方法已不能满足实际生产和科研的需要。根据流动单元理论，把油气藏空间看作为一系列的毛细管，应用Kozeny-Carman方程得到的流动单元指数（FZI），再对储层进行分类。通过对FZI与测井响应的单相关分析，筛选出对FZI反映灵敏的测井响应及响应组合。在具体研究中，选择了校正后的声波时差、自然伽马与密度的比值、深浅侧向电阻率比值为变量，建立了FZI的多参数拟合方程，实现了用测井响应曲线来评价FZI的方法。在此基础上，建立了以流动单元分类为基础的较为精细的非均质储层渗透率评价模型。通过在塔里木油田的现场应用，与未进行分类的碳酸盐岩储层相比，该方法极大地提高了储层渗透率计算的精度，取得了较好的应用效果。     

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.     

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裂缝系统中流体性质的变化，必然引起地震波特征的变化；裂缝系统中含气或含水对纵横波的影响是不相同的，譬如，含气时纵波速度明显降低，而横波速度保持不变或略有降低，纵横波速度比和泊松比则明显降低。利用在四川灵音寺地区采集得到的多分量地震资料，处理得到纵波和转换波剖面，并反演出纵横波速度等信息；经过纵横波剖面的直接对比、以及对道积分、速度剖面的综合对比解释，预测出了该区试验测线范围内的含气分布，预测结果与实钻吻合较好。初步实践表明，利用多波资料直接检测油气是一种有效方法。     

应用声波全波列测井计算裂缝性含气储层参数

裂缝性气层的解释目前仍存在许多困难 ,主要原因是测井特征微弱 ,不易识别。在文献总结的基础上 ,采用纵波首波幅度比值法、合成横波时差重叠法、纵横波速度比法、等效弹性模量差比法和三孔隙度重叠法等方法定性识别气层 ,其目的在于通过突出气层的测井特征来识别气层。引入弹性模量差比计算含气饱和度 ,并用一口井的核磁测井差谱结果进行了对比验证。没有横波资料的井 ,通过引入纵、横波地区经验关系来获得横波。应用提取的斯通利波也可以计算渗透率。某油田 7口井的处理结果与地质实际吻合 ,表明这种方法切实可行 ,可以全面评价裂缝性气层。     

An improved technique for modeling initial reservoir hydrocarbon saturation distributions: applications in Illinois (USA) Aux Vases oil reservoirs

An improved technique for modeling the initial reservoir hydrocarbon saturation distributions is presented. In contrast to the Leverett J-function approach, this methodology (hereby termed flow-unit-derived initial oil saturation or FUSOI) determines the distributions of the initial oil saturations from a measure of the mean hydraulic radius, referred to as the flow zone indicator (FZI). FZI is derived from porosity and permeability data. In the FUSOI approach, capillary pressure parameters, S_wir, P_d, and λ, derived from the Brooks and Corey (1966) model [Brooks, R.H., Corey, A.T., 1966. Hydraulic properties of porous media, Hydrology Papers, Colorado State Univ., Ft. Collins, No. 3, March.], are correlated to the FZI. Subsequent applications of these parameters then permit the computation of improved hydrocarbon saturations as functions of FZI and height above the free water level (FWL). This technique has been successfully applied in the Mississippian Aux Vases Sandstone reservoirs of the Illinois Basin (USA). The Aux Vases Zeigler field (Franklin County, IL, USA) was selected for a field-wide validation of this FUSOI approach because of the availability of published studies. With the initial oil saturations determined on a depth-by-depth basis in cored wells, it was possible to geostatistically determine the three-dimensional (3-D) distributions of initial oil saturations in the Zeigler field. The original oil-in-place (OOIP), computed from the detailed initialization of the 3-D reservoir simulation model of the Zeigler field, was found to be within 5.6% of the result from a rigorous material balance method.     

利用模型正演优选地震属性进行储层预测

利用地震属性预测储层会产生不确定性,为此提出了利用岩心、测井和叠后地震属性预测储层的新方法。该方法是以岩心物性参数和岩石物理性质正演模拟为基础,分析岩石物性变化引起的地震响应特征变化,利用优选的地震属性进行储层预测。这套技术以正演为主,正、反演相结合,减少了预测结果的多解性,提高了用属性预测储层的准确性。该方法操作步骤包括横波速度预测、流体替换、属性优选、属性分类和储层预测等。用该方法预测了砂体分布及含油级别,其结果与钻井结果一致,取得了满意的效果。     

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.     

利用XMAC测井资料识别油气层段

XMAC全波资料在油气勘探及油田工程中有着广泛的应用，在研究全波传播的理论基础上，提出了XMAC测井资料识别油气的方法，即：原始波形识别油气法，纵横波比法识别油气和纵横波能量衰减法识别油气，在油田取得了较好的应用效果。     

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塔里木盆地地质情况复杂,油气类型多种多样,目前已在多套层系发现了凝析油气藏;利用常规测井资料解释评价油气层,能够有效地识别并确定油气层和水层的界限,但却无法区分气层还是油层,以及如何准确划分油、气界面。电缆重复地层测试测井通过测量地层压力,建立压力—深度剖面,根据其压力梯度变化估算出地层流体性质,划分油、气水界面;中子、密度测井测量的地层含氢指数与地层介质的电子密度指数由于氢元素的存在而体现了很好的相关性,利用该相关性质可以确定地层液体含量（孔隙度）,识别流体类型;偶极横波成像测井提供了当今测量地层纵波、横波和斯通利波的最好方法,通过对全波的处理,可以提取大量的岩石机械特性,由于气层对纵横波速度的影响不同,因此可以利用纵横波速度比、泊松比来直观识别油气层,划分油、气界面。通过对以上测井方法基本理论的阐述,并在实例中应用新方法解决了常规测井资料无法解决的难题。研究结果表明,利用测井新技术、新思路,结合常规测井资料能够有效识别凝析气层,准确划分油气界面。