Extending the Application of a Shale Volume Estimation Formula Derived from Factor Analysis of Wireline Logging Data

A multivariate statistical procedure is developed for the estimation of shale volume in clastic sedimentary formations. The method offers an alternative for extracting the shale content from borehole geophysical measurements. Factor analysis of various well-logging data types generates a new well log that correlates with the shale content of shaly-sandy rocks. The mathematical relationship between shale volume and factor scores is represented by a nonlinear equation, which seems to be applicable for data sets originating from different sedimentary basins. A comparative study is made between three different data sets originating from Hungary and the United States of America in order to check the validity of the proposed empirical formula. Shale volumes predicted from factor analysis are confirmed by estimates from independent deterministic and inverse modeling. Petrophysical information derived by factor analysis of logs recorded in deep wells can be used for a more accurate and reliable estimation of effective porosity and absolute permeability of reservoir rocks, for decreasing the estimation error of inversion estimates and for reducing the ambiguity in the solution of the well-logging inverse problem.     

Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin,India

Well log analysis provides the information on petrophysical properties of reservoir rock and its fluid content. The present study depicts interpretation of well log responses such as gamma ray, resistivity, density and neutron logs from six wells, namely W-1, W-2, W-9, W-12, W-13 and W-14 under the study area of Krishna-Godavari (K-G) basin. The logs have been used primarily for identification of lithology and hydrocarbon-bearing zones. The gamma ray log trend indicates deposition of cleaning upward sediment. Coarsening upward, clayey-silty-sandy bodies have been evidenced from the gamma ray log. Gas-bearing zones are characterised by low gamma ray, high deep resistivity and crossover between neutron and density logs. Total 14 numbers of hydrocarbon-bearing zones are identified from wells W-9, W-12, W-13 and W-14 using conventional log analysis. Crossplotting techniques are adopted for identification of lithology and fluid type using log responses. Crossplots, namely P-impedance vs. S-impedance, P-impedance vs. ratio of P-wave and S-wave velocities (Vp/Vs) and lambda-mu-rho (LMR), have been analysed to discriminate between lithology and fluid types. Vp/Vs vs. P-impedance crossplot is able to detect gas sand, brine sand and shale whereas P-impedance vs. S-impedance crossplot detects shale and sand trends only. LMR technique, i.e. λρ vs. μρ crossplot is able to discriminate gas sand, brine sand, carbonate and shale. The LMR crossplot improves the detectability and sensitivity of fluid types and carbonate lithology over other crossplotting techniques. Petrophysical parameters like volume of shale, effective porosity and water saturation in the hydrocarbon-bearing zones in these wells range from 5 to 37%, from 11 to 36 and from 10 to 50% respectively. The estimated petrophysical parameters and lithology are validated with limited core samples and cutting samples from five wells under the study area.     

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.     

用测井资料确定储层三孔隙组分的理论与方法

分析了岩石的孔隙和孔隙结构与岩石的成分尤其是泥质和粘土之间的密切联系,并从常规测井、核磁测井反映的孔隙度、泥质含量等参数入手,研究建立了常规测井资料划分三种孔隙组分的理论和实现方法,结合实际测井资料,对这些方法进行了检验。实例表明,根据该理论和方法确定的储层的三孔隙组分——粘土水孔隙、微孔隙水孔隙以及自由水孔隙组分可以取得较好的效果。     

Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin,India

Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance (AAI) and relative acoustic impedance (RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone (TS) and Barail Arenaceous Sandstone (BAS) are the main reservoirs, delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network (MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.     

核测井曲线高分辨率处理技术及其应用

着重探讨了放射性测井曲线中自然伽马测井曲线、补偿中子测井曲线和补偿密度测井曲线的高分辨率处理方法及其应用效果。对于自然伽马及自然伽马能谱测井曲线，采用反褶积技术和有效的噪音压制技术；对于补偿中子测井曲线和补偿密度测井曲线采用了改进的α因子法。可以发现：高分辨率处理技术可明显地改善有关曲线的纵向分层能力；处理后的曲线与岩心柱状图及与微球、微电极等高分辨率曲线进行对比，其相关性有很大的改善；应用高分辨率处理后的曲线求薄层的泥质含量等参数，其相对误差明显下降。     

The TST3D Method for Automated Structural Interpretation in Horizontal Wellbores

Horizontal wells dominate the development of unconventional shale reservoirs. Using real time drilling data to steer in a target zone is the key to economic success. Today structural interpretation in unconventional horizontal wells is a manual process that is time-consuming, tedious, and error-prone, especially because gamma-ray (GR) logs are commonly the only available logging-while-drilling data. For the first time, a method named TST3D is developed to automate interpretation of subsurface structure. TST3D (true stratigraphic thickness in three-dimensional space) automates structural interpretation using pattern recognition. Given an initial structural model, TST3D automatically computes true stratigraphic thickness (TST) as the shortest distance from each wellbore survey location to the initial surface, then matches GR patterns in the horizontal well to those seen in a vertical pilot well in TST domain. TST3D inserts fold hinges, bends the structure, then recomputes the modeled GR response, progressively matching the pilot well log signature, from heel to toe in the horizontal well. There are three assumptions in the current version of TST3D: constant layer thickness across the drilled interval, GR variation follows stratigraphic layering, and no faults are present in the drilled section. Those assumptions are reasonable in most shale plays. The TST3D method can be applied in either a post-drill mode for structural interpretation or real-time mode for aiding geosteering. Field tests in different shale plays and complex well trajectories demonstrate that TST3D runs quickly: a structural model of a 10,000-ft horizontal section can be computed in minutes, and a real-time update of 100 ft of new data takes less than a minute. Automating the geosteering correlation process would allow well placement engineers to cover multiple wells simultaneously, increasing the efficiency of the team while potentially improving service quality.

     

Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas

As a neural network provides a non-linear function mapping of a set of input variables into the corresponding network output, without the requirement of having to specify the actual mathematical form of the relation between the input and output variables, it has the versatility for modeling a wide range of complex non-linear phenomena. In this study, groundwater contamination by nitrate, the ANNs are applied as a new type of model to estimate the nitrate contamination of the Gaza Strip aquifer. A set of six explanatory variables for 139 sampled wells was used and that have a significant influence were identified by using ANN model. The Multilayer Perceptrons (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Linear Networks were used. The best network found to simulate Nitrate was MLP with six input nodes and four hidden nodes. The input variables are: nitrogen load, housing density in 500-m radius area surrounding wells, well depth, screen length, well discharge, and infiltration rate. The best network found had good performance (regression ratio 0.2158, correlation 0.9773, and error 8.4322). Bivariate statistical test also were used and resulting in considerable unexplained variation in nitrate concentration. Based on ANN model, groundwater contamination by nitrate depends not on any single factor but on the combination of them.     

随机分形在刻划储层非均质特性中的应用

储集层的非均质性质普遍存在于砂泥岩储层、碳酸盐岩和火成岩储层中。线性数学手段难以精确描述非均质储层参数的空间分布特征。在详细分析砂泥岩地层参数非均质特性的基础上,将分形方法与克里金方法相结合来刻划储层密度和声波时差的分布规律,通过对两口井的相应参数进行插值产生的虚测井值与真实测井值吻合良好。在井距合理的情况下,统计分形方法能够较好地刻划储集层的非均质性。     

页岩水平井体积压裂设计的一种新方法

页岩水平井常采用体积压裂技术获得产能,压裂形成的缝网体积、渗透率是影响压裂效果的关键因素。目前页岩体积压裂设计借用产能预测模型优化缝网参数,此模型较复杂,不便于现场应用。根据等效渗流原理,将页岩储层压裂后形成的缝网系统等效为一个高渗透带,建立了体积压裂缝网参数与施工规模关系模型,提出了体积压裂设计的3个步骤:体积压裂可行性研究、数值模拟优化缝网参数和施工参数优化。根据QY2页岩油水平井特征,进行了体积压裂设计和现场实施。结果表明:压裂形成的高渗透带对产能的贡献最大;高渗透带数量、体积和渗透率增加,压裂后的累积产量和采出程度逐渐增加,存在最优的高渗透带参数。现场应用表明这种设计方法方便实用,可以推广。     

Characterization of the Tertiary reservoir in Khabbaz Oil Field,Kirkuk area,Northern Iraq

Tertiary reservoir represented by Jeribe, Anah, Azkand, and Azkand/Ibrahim formations has been studied from reservoir characterization point of view in Khabbaz Oil Field/Northern Iraq. Examined thin sections prepared from core rock samples which are selected from the wells Kz-8 and Kz-9 revealed the existence of different microfacies in which are varies types of porosity such as interparticle, intraparticle, moldic, vuggy, and fractures have been noticed. In addition to these different types of diagenetic features are represented mainly by cementation, dolomitization, recrystallization, and dissolution. The available log data for the wells Kz-11, Kz-14, and Kz-16 have been used in characterizing the reservoir properties of the studied formations. The detected lithologies from porosity logs showed domination of limestone, dolomitic limestone, and dolomites in addition to few thin beds of anhydrites in Jeribe Formation and thin beds of sand dominated lithologies in Azkand Formation. Clay content has been calculated from gamma ray log along the studied sections and appeared to be mostly of dispersed clay type as shown from density-neutron porosity crossplot of Thomas-Stieber. Correction from the effect of clay content has been done for all the measured sonic, density, and neutron porosities. The available porosity and permeability values which are measured from core tests in the well Kz-9 for the formations of Jeribe, Anah, and Azkand have been adapted to formalize the best equation for calculating permeability in the other studied wells which have no core test measurements but only log data (Kz-11, Kz-14, and Kz-16 wells). Depending on porosity, permeability, and shale content, the studied sections have been subdivided into seven reservoir units in the wells Kz-11 and Kz-16, and for eight reservoir units in the well Kz-14.     

An evaluation of aquifer vulnerability in two nitrate sensitive areas of Catalonia (NE Spain) based on electrical resistivity methods

The protection of groundwater has become one of the most important European environmental policies as evidenced by the Orders relating to the protection of water from contamination, Directive 2000/60/CE of the European Parliament and the European Union Council, and more concretely Directive 2006/118/118/CE, related to the protection of groundwater from pollution and degradation. Traditional methods for assessing vulnerability include soil surveys, drilling and analysis of lithology logs from wells with the objective of characterising the thickness, hydraulic properties and lateral extend of the protective layers. However, such studies can be labour-intensive and expensive. In addition, the parameters measured may have high spatial variability, which makes accurate characterization over large areas difficult. Fortunately, a numerical index of protection can be assigned from the longitudinal electrical conductance parameter derived from electrical resistivity surveys (VES, ERT or any other electrical or EM method). This can be more accurate and reliable than any other vulnerability index derived only from visual inspection or interpolated from sparse borehole data.     

Hydrocarbon volume optimization using improved net-to-gross estimates through core-log calibration of the “Akar field,” Niger Delta

Net to gross (NTG) is an important parameter in evaluating the volumes of hydrocarbon in place (VHIP) in reservoirs, and proper evaluation of this parameter will lead to significant accuracy in the estimated reserves. The use of conventional petrophysical log evaluations often does not provide sufficient resolution for net sand analysis of reservoirs, especially in laminated reservoir rocks. The uncertainties associated with log-derived net-to-gross estimates arise from the petrophysical shale volume (V _sh) cutoffs used in deriving the net sand count over a reservoir interval. One way of improving the net-to-gross estimates is by using a model that calibrates log-derived net to gross to a core equivalent, which is the more accurate representation of net sand counts. In this study, a model for calibrating log-derived net to gross to a core equivalent based on genetic units and facies associations of reservoir rocks in three wells (wells 7, 36, and K008) was established. Ultraviolet core photographs show a good contrast between hydrocarbon-stained sandstones and shales, and combining it with white light-slabbed core images facilitated a manual net sand count of the core photographs on a bed-by-bed basis. Petrophysical shale volume (V _sh) cutoff derived from two volumes of shale indicators was applied to generate net sand counts, which were used to get log-derived net-to-gross values. Then, the net to gross from core images and petrophysical clay volume (V _sh) analysis were compared by facies associations, and this comparison yielded a reliable core-calibrated net-to-gross model, which reduces the uncertainties in net-to-gross values estimated from V _sh cutoffs. The results show that for the distributary channel sands, net to gross derived using V _sh cutoff results in an underestimation of net to gross by about 6–10 % when compared with the core-calibrated net to gross, while for the upper shoreface units, V _sh cutoff overestimates net to gross by about 7–10 % when compared to its core-calibrated equivalent.     

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model-based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model-based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi-layer feed-forward network (MLFN) and radial basic function network (RBFN).     

利用常规测井资料基于岩石物理和多矿物分析反演横波速度

测井横波速度是测井地震联合反演的重要标定参数.为克服大量老井缺少横波速度资料和现有横波速度估算方法的不足, 基于孔隙介质岩石物理理论, 通过常规测井资料求取多矿物组分, 利用VRH模型求得地层的等效弹性模量; 最后利用纵波速度作为约束条件, 根据Biot-Gassmann方程得到地层横波速度.计算结果与实测结果对比表明, 平均相对误差限在5%左右, 与Xu-White模型相比, 该方法物理意义更为明确, 使用更简便, 计算精度提高一倍左右.      

Correlation of geologic logs with spectral methods

Spectral studies of geologic logs demonstrate that automatic well—log correlation can be processed more efficiently in the frequency domain. Cross correlation of the power spectra of well logs identifies the direction and degree of thickening of stratigraphic sequences between two wells. Given the stretch, the displacement between logs is computed by correlation processes without relying on iterative procedures. Beginning with digitized log data of unequal lengths, power spectra are computed. The stretch factor between the two logs is observed as a difference in frequency scaling. A transform to logarithmic frequencies converts the spectra to a form that reduces the scaling effect of the frequencies to a simple displacement between the plots. A Lagrange interpolation procedure permits cross correlation of the two spectra with a variable window size. The peak value of the resultant correlation function identifies the displacement between the spectra and this, in turn, permits calculation of the stretch factor.     

Delineation of capture zones for municipal wells in fractured dolomite, Sturgeon Bay, Wisconsin, USA

A wellhead protection study for the city of Sturgeon Bay, Wisconsin, USA, demonstrates the necessity of combining detailed hydrostratigraphic analysis with groundwater modeling to delineate zones of contribution for municipal wells in a fractured dolomite aquifer. A numerical model (MODFLOW) was combined with a particle tracking code (MODPATH) to simulate the regional groundwater system and to delineate capture zones for municipal wells. The hydrostratigraphic model included vertical and horizontal fractures and high-permeability zones. Correlating stratigraphic interpretations with field data such as geophysical logs, packer tests, and fracture mapping resulted in the construction of a numerical model with five high-permeability zones related to bedding planes or facies changes. These zones serve as major conduits for horizontal groundwater flow. Dipping fracture zones were simulated as thin high-permeability layers. The locations of exposed bedrock and surficial karst features were used to identify areas of enhanced recharge. Model results show the vulnerability of the municipal wells to pollution. Capture zones for the wells extend several kilometers north and south from the city. Travel times from recharge areas to all wells were generally less than one year. The high seasonal variability of recharge in the study area made the use of a transient model necessary. Electronic Publication     

Forward modeling and inversion of induction logs from shaly sand reservoirs using petrophysical conductivity models

Algorithms and software have been designed for petrophysical 2D forward simulation and inversion of induction logs from oil wells drilled in shaly sandstone using conductivity models for different clay types. As comparative analysis of relative amplitudes and phases of tool responses has demonstrated, it is possible to reliably and accurately estimate fluid saturation and porosity from induction logs. The applied 2D numerical inversion of both theoretical and well logs yielded quantitative estimates of petrophysical parameters of reservoirs, with clay volume fraction assumed according to the chosen conductivity model. Correction for the presence of clay improves inversion quality if clay content as well as type are taken into account.     

利用测井资料计算机自动识别沉积微相方法研究

以关键井沉积微相分析为基础,将测井学同地质、现代数学相结合,从测井资料中提取反映各种微相的测井特征参数,并应用一套多元统计分析方法,建立各种沉积微相的判别模式,研究开发一套软件系统,实现计算机根据测井资料自动分析判别陆相地层沉积微相。应用该软件对某油田西区的十几口井的测井资料进行处理,并绘制出沉积微相平面图。     

Analysis of groundwater level trend in Jakham River Basin of Southern Rajasthan

Groundwater accounts for about half of the water use for irrigation in India.The fluctuation pattern of the groundwater level is examined by observing rainfall replenishment and monitoring wells.The southern part of Rajasthan has experienced abrupt changes in rainfall and has been highly dependent on groundwater over decades.This study presents the impact of over-dependence on groundwater usage for irrigation and other purposes,spatially and temporally.Hence,the objective of this study is to examine the groundwater level trend by using statistical analysis and geospatial technique.Rainfall factor was also studied in groundwater level fluctuation during 2009-2019.To analyze the influence of each well during recharge or withdrawal of groundwater,thiessien polygonswere generated from them.In the Jakham River basin,75 wells have been identified for water level trend study using the Mann-Kendall statistical test.The statistics of trend analysis show that 15%wells are experiencing water level decline in pre-monsoon,while very low percentage of wells have such trend during post-monsoon season.The average rate of water level decline is 0.245 m/a in pre-monsoon and 0.05 m/a in post-monsoon.The aquifer recharge potential is also decreasing by year.it is expected that such type of studies will help the policy makers to adopt advanced management practices to ensure sustainable groundwater resource management.