Porosity Logs Research Articles

The effect of shale properties on the anisotropic brittleness criterion index from laboratory study

Shale anisotropy has a significant impact on the brittleness index and hydraulic fracturing. First of all, we improve the Rickman's brittleness index by introducing the anisotropy parameters and angle parameters based on Rickman's brittleness index (BI). Then the anisotropic brittleness criterion index (BCI) is proposed by introducing failure criteria parameters to balance the shortcomings of the conventional BI index. The new brittleness index is applied to the laboratory measurements of samples of Santa Maria Basin shale formation. The anisotropic BCI might be closer to the actual value of brittleness in the fracturing process by considering the stress of crack expansion. The value of the BCI increases as porosity decreases, thus the porosity log is an effective parameter and can help us establish a good BI or BCI predication rock models. We show that total organic carbon (TOC) is not reliable for predicting BI or BCI because there is no linear relationship between BI, BCI and TOC. It is interesting that delta (δ) is more sensitive to BCI than epsilon (e) and gamma (γ). It appears to have a good linear relationship with the BCI. It might be reliable to predict the BCI of shale formation of the Santa Maria Basin by δ. By introducing the relationship between pressure and BCI, we find that shale samples with different TOC show regular changes of brittleness index under different pressures. The effect of stress needs to be considered in the analysis of BCI and one must choose the appropriate stress for fracturing.

Accurate Estimation of Petrophysical Indexes by RBF, ANFIS, and MLP Networks

Production and management of oil and gas in today’s highly competitive environment require the use of high tech tools. These tools provide the means by which the cost of exploration, production, and management of hydrocarbon resources may be reduced. Petrophysical characteristics of underlying formation have an important role in reservoir management and drilling wells. One of the most common ways to reach this information is well log analysis. Meanwhile, sometimes well logging does not implement well or some log data are accompanied by many errors. Thus, highly skilled experts and laboratory information are needed for interpretation and evaluation of data. Therefore, designing a model that is able to evaluate the petrophysical index using well log data without laboratory information will be very economical. In this study, after selecting the best logs (minimum information and maximum accuracy) some parameters, such as porosity, saturation, sonic, and density logs, were predicted by adaptive neuro fuzzy intelligence system, radial basis function, and artificial neural network models. Finally, the best models for each parameter were optimized and optimal epoch, neuron, function, and spread clarified. In fact, due to these new models, some important parameters of formations were predicted well and cost and time of data gathering were reduced.

Simultaneous optimization of multiple objective functions for reservoir modeling

We have developed a new method, a synthetic validity test, and a field data demonstration for constructing reservoir models that simultaneously match available seismic, borehole, and geologic data in an optimal way. We combine geostatistical simulation with simultaneous nonlinear stochastic optimization of multiple objective functions, and we do not require the selection of weighting schemes or multiple optimization computational runs. Because each geologic and geophysical data set has its own strengths and weaknesses, realistic models are best obtained by simultaneously matching all of the multiple data constraints. We define a set of objective functions for each of the multiple data sets that are used to constrain the reservoir-modeling optimization. In the examples, we specify an [Formula: see text]-norm misfit to match inverted seismic attribute volumes, an [Formula: see text]-norm to match lithofacies and porosity logs, and the Hausdorff metric to match image patterns in geologic map-based information. We use a geostatistical technique to initialize a population (ensemble) of starting models and run a nonlinear evolutionary algorithm to drive the ensembles of model solutions toward the Pareto optimal front, along which represents the best-compromise set of model solutions that simultaneously satisfy all of the multiple objectives. We tested our method on a 3D synthetic object-oriented reservoir model for which variogram-based simulation techniques typically fail to reproduce realistic models. We applied our method on a producing reservoir located offshore Western Australia. Our results indicate that the new method is capable of producing optimal models of reservoir properties and flow-unit connectivity that are consistent with known reservoir information, including dynamic production data such as pressure interference tests, and 4D seismic monitoring data.

Estimating shear wave velocities in oil fields: a neural network approach

In this study, we applied the back-propagation Artificial Neural Network (ANN) technique to test the shear-velocity for the two wells from an oil field in southeastern region of Turkey estimated from an empirical relationship. The input to the neural network includes neutron porosity, density, true resistivity, P-wave velocity and gamma-ray logs which are known to affect the shearwave velocity. The correlation between the shear-wave velocity from the empirical relationship and that from the neural network is close to one in both the training and testing stages. Thus, the ANN technique can be used to predict shear-wave velocity from other well log data.

Prediction of permeability anisotropy using Dar-Zarrouk parameters of deep resistivity logs: a case study in the northern Western Desert of Egypt

This study is a trial to predict the relationship between the formation resistivity data and some petrophysical properties of rocks in Tut and Khalda oil fields at the northern Western Desert, Egypt. First, the deep resistivity (deep laterolog (LLD)) and gamma ray (GR) logs are smoothed by a linear combination of simple fitting functions that satisfy the smoothness criteria, and consequently the behavior of both logs can be simulated. Next, the smoothed log data are divided into branches, where high and frequent excursions in GR and LLD logs occur. In subsequent steps, the Dar-Zarrouk parameters are calculated and followed by a smoothing process for each branch. Based on a visual inspection of these parameters with effective porosity logs, the Dar-Zarrouk parameters are divided into zones and then interpreted in terms of shale content and the potentiality of the two oil fields. The permeability trend observed in core samples showed that the calculated parameters provide an adequate qualitative prediction for the petrophysical properties including the overall permeability trends. Despite the fact that the predicted permeability trends in Khalda oil field coincides with those measured from core samples, a weak correlation is observed, which can be attributed to the reservoir heterogeneity with dominance of laminated and dispersed shale and/or iron oxides. The potentiality of the proposed method lies in its simplicity, requiring only GR, LLD, and effective porosity logs. However, it would be more meaningful if the above method is tested in areas with diverse geological environment.

Application of Optimized Neural Network Models for Prediction of Nuclear Magnetic Resonance Parameters in Carbonate Reservoir Rocks

Abstract —Neural network models are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went to step forward by optimizing neural network models using three intelligent optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), and ant colony (AC), to eliminate the risk of being exposed to local minima. This strategy was capable of significantly improving the accuracy of a neural network by optimizing network parameters such as weights and biases. Nuclear magnetic resonance (NMR) log measures some of the most useful characteristics of reservoir rock; the capabilities of the optimized models were used for prediction of nuclear magnetic resonance (NMR) log parameters in a carbonate reservoir rock of Iran. Conventional porosity logs, which are the easily accessible tools compared to NMR log’s parameters, were introduced to the models as inputs while free fluid porosity and permeability, which were measured by NMR log, are desire outputs. The performance of three optimized models was verified by some unseen test data. The results show that PSO-based network and ACO-based network is the best and poorest method, respectively, in terms of accuracy; however, the convergence time of GA-based model is considerably smaller than PSO-based and GA-based models.

Detecting vuggy porosity in carbonate reservoirs by well logs

Vuggy porosity is a type of porosity in carbonate rocks. This kind of porosity significantly affects the permeability, pressure drop and recovery factor in the reservoir. Therefore, their identification and modeling is important for the reservoir characterization and history matching. Image logs, RCAL and SCAL tests are main tools to determine vugs, though all are costly and sometimes unavailable. Thus, development of a generalized approach to recognize vugs in reservoirs will be significant. In this paper, a combined majority voting-Parzen–wavelet based algorithm is developed using gamma ray (GR), neutron porosity (NPHI), bulk density (RHOB) and sonic (DT) logs for identification of vugs in frequency domain. The agreement between the core tests results and the results of this newly developed method confirms the application of proposed new method.

Imaging and characterization of facies heterogeneity in an alluvial aquifer using GPR full-waveform inversion and cone penetration tests

Summary Spatially highly resolved mapping of aquifer heterogeneities is critical for the accurate prediction of groundwater flow and contaminant transport. Here, we demonstrate the value of using full-waveform inversion of crosshole ground penetrating radar (GPR) data for aquifer characterization. We analyze field data from the Krauthausen test site, where crosshole GPR data were acquired along a transect of 20 m length and 10 m depth. Densely spaced cone penetration tests (CPT), located close to the GPR transect, were used to validate and interpret the tomographic images obtained from GPR. A strong correlation was observed between CPT porosity logs and porosity estimates derived from GPR using the Complex Refractive Index Model (CRIM). A less pronounced correlation was observed between electrical conductivity data derived from GPR and CPT. Cluster analysis of the GPR data defined three different subsurface facies, which were found to correspond to sediments with different grain size and porosity. In conclusion, our study suggests that full-waveform inversion of crosshole GPR data followed by cluster analysis is an applicable approach to identify hydrogeological facies in alluvial aquifers and to map their architecture and connectivity. Such facies maps provide valuable information about the subsurface heterogeneity and can be used to construct geologically realistic subsurface models for numerical flow and transport prediction.

Porosity Parameters Quantitatively Predicted of Ultra-Low Permeability Reservoir in Hejiaji Area

Using the acoustictime logging data to establish reasonable and effective porosity parameter quantitative prediction model, can predict quality reservoir and favorable petroliferous province. Chang 6 formation in Hejiaji area is taken as an example to studing the reservoir physical properties and logging response characteristics, and the acoustictime logging and core analysis porosity are used to set up porosity logging interpretation model, then acculate porosity according to the model. The results show that after the core place treatment, the porosity quantitative prediction model established by acoustictime and layer point analysis porosity has high precision. To verify the porosity logging interpretation model of the calculated porosity and core analysis porosity, the application effect is good.

Reducing predictive uncertainty in log-derived water saturation models in a giant tight shaly sandstones – A case study from Mesaverde tight gas reservoir

A log-derived calibration coefficient is introduced to calibrate conventional water saturation models in tight shaly sandstone reservoirs in the Rocky Mountains, western United States, in this study.It is implemented for calibration of eight traditional saturation models in this giant gas field to prove its validity. The application of this calibration coefficient indicates that it is a simple and strong tool for improving of water saturation estimation in tight shaly sandstones, particularly for zones with high GR and/or low true resistivity. The proposed calibration coefficient is based upon an expression which depends on true resistivity log, neutron and density logs to take total porosity and gamma ray log. It has a significant advantage i.e. independency from electrical rock properties (Archie's parameters): m (cementation factor), n (saturation exponent) and a (tortuosity factor). It means that the variety of them cannot affect the operation of the introduced calibration coefficient.In this study, the calibration results of the eight traditional water saturation models are compared with core results for 2579 data points that are taken from fourteen wells (39 different zones) from Mesaverde in five western USA basins. This study shows that the new approach that is introduced is more efficient as it reduces the uncertainty associated with water saturation estimation in zones with high GR and/or low true resistivity than zones with low GR and/or high true resistivity in tight shaly sandstones.

Integrated study of seismic and well data for porosity estimation using multi-attribute transforms: a case study of Boonsville Field, Fort Worth Basin, Texas, USA

This study represents part of a research project focused on the Runaway Surface (MFS53), which is one of the reservoir levels in the Boonsville Field of the Fort Worth Basin in north central Texas, USA. This reservoir system is a subunit of the Bend Conglomerate section, which is a productive series of gas reservoirs deposited during the Middle Pennsylvanian fluvio-deltaic environment. This paper adopts an integrated approach to the seismic and well log data, using a combination of geostatistical and multi-attribute regression transform methods. The main focus of the research is to accurately predict the porosity distribution of the Runaway Formation away from the well locations. The input data consists of 32 wells (of which six wells contain porosity logs) together with a 3D seismic volume of Boonsville Field. The 3D seismic volume was inverted to obtain the acoustic impedance cube of the study area. Secondly, six multi-attribute data slices were extracted from both surface seismic and inverted acoustic impedance volumes. Subsequently, the porosity distribution of a selected reservoir level was estimated over the full extent of the study area using both acoustic impedance alone, and multi-attributes. Initially, the multi-attribute transform algorithm was trained using the well log data. The porosity at each well location was averaged over a particular depth zone of interest, and then compared with six extracted attribute slices averaged over the same depth window. In order to select the appropriate number of attributes for analysis, a cross-validation process was followed. The results of this cross-validation process and the training of the multi-attribute transforms were applied to the extracted attribute slices in order to produce the final porosity map of the Runaway Formation. The cross-plot between the seismically derived porosity and the well porosity values showed that accuracy of porosity prediction was increased from 75 %, when using a single attribute (acoustic impedance (AI)), to 90 % when multiple attributes are used. Additionally, when the actual well-derived porosities were overlaid onto the final predicted porosity maps from both techniques, a significant amount of mismatching was observed on the porosity map derived from AI alone, whereas the predicted porosity with the multi-attribute regression transform was a close match to the actual well-derived porosities. Beside this, the subsurface geology (i.e., karst collapse features) were not clear in the porosity map deduced from AI. Based on these cross-correlation results, the porosity map derived from multi-attribute regression transform was selected. The high level of correlation (90 %) with the actual and derived porosity indicates that the seismic multi-attributes were reliably transformed to the reservoir porosity log. The derived porosity map for the Runaway Formation indicates high lithological variation within the reservoir level with a porosity generally varying between 2 and 32 %. The western portion of the Runaway Formation is highly porous and can be considered for future exploration purposes. Although this study retains a certain level of uncertainty, which can be attributed to the well and seismic data used, due to data limitations, uncertainty analysis was not included in the current study, but this should be considered in future studies so as to improve the porosity prediction.

Textural and pore size analysis of carbonates from integrated core and nuclear magnetic resonance logging: An Arbuckle study

The T2 relaxation times recorded by nuclear magnetic resonance (NMR) logging are measures of the ratio of the internal surface area to volume of the formation pore system. Although standard porosity logs are restricted to estimating the volume, the NMR log partitions the pore space as a spectrum of pore sizes. These logs have great potential to elucidate carbonate sequences, which can have single, double, or triple porosity systems and whose pores have a wide variety of sizes and shapes. Continuous coring and NMR logging was made of the Cambro-Ordovician Arbuckle saline aquifer in a proposed CO2injection well in southern Kansas. The large data set gave a rare opportunity to compare the core textural descriptions to NMR T2 relaxation time signatures over an extensive interval. Geochemical logs provided useful elemental information to assess the potential role of paramagnetic components that affect surface relaxivity. Principal component analysis of the T2 relaxation time subdivided the spectrum into five distinctive pore-size classes. When the T2 distribution was allocated between grainstones, packstones, and mudstones, the interparticle porosity component of the spectrum takes a bimodal form that marks a distinction between grain-supported and mud-supported texture. This discrimination was also reflected by the computed gamma-ray log, which recorded contributions from potassium and thorium and therefore assessed clay content reflected by fast relaxation times. A megaporosity class was equated with T2 relaxation times summed from 1024 to 2048 ms bins, and the volumetric curve compared favorably with variation over a range of vug sizes observed in the core. The complementary link between grain textures and pore textures was fruitful in the development of geomodels that integrates geologic core observations with petrophysical log measurements.

Dictionary of petroleum exploration, drilling & production

Contents: Drilling & completion records Rotary drilling rig Cable tool drilling rig Crank counterbalanced bean pumping unit Geological time scale Giant oil & gas fields Mineral properties Sandstone limestone classifications Carbonate porosity Common geological map & log symbols Drill stem test symbols Flow sheet symbols Land subdivisions (U.S.) Geological features (U.S. & Canada) Unit conversion factors Atomic weights & numbers Greek alphabet.

The Technique of Seismic Inversion and Use of the Relation Between Inversion Results and Porosity Log for Predicting Porosity of a Carbonate Reservoir in a South Iranian Oil Field

Determination of petrophysical parameters by using available data has a specific situation in exploration and production study in the oil and gas industries. For example, estimating of corrected porosity as a petrophysical parameter can help in decisions that have high financial risk, such as drilling. Therefore, the study of seismic data, extraction of seismic attributes from them, and incorporation of these attributes by well log data is an important key for detection of reservoir properties. Acoustic impedance a key parameter for seismic and direct geologic interpretation and for detecting lateral lithologic variation, can be defined as the product of density and velocity. Seismic inversion is used to estimate detailed seismic and rock properties, such as acoustic impedance and porosity, from seismic data. The method is widely used and proven successful by the petroleum industry, but has thus far not been widely adopted for academic studies. Acoustic impedance data and the derived porosity distribution facilitate detailed studies of lithology, compaction, and fluid flow in the shallow subsurface. A comparison of reflection and impedance data support the notion that seismic reflection events arise from bed boundaries rather than from lateral changes in impedance, even where these are significant. The main result of the seismic inversion study is a profile showing the absolute variations in acoustic impedance and porosity along wells. This article outlines a workflow and reports the application of model-based seismic inversion and its relation with porosity in Balal oil field in the south of Iran.

Log-derived thickness and porosity of the Barnett Shale, Fort Worth basin, Texas: Implications for assessment of gas shale resources

This study estimates reservoir quality and free-gas storage capacity of the Barnett Shale in the main natural-gas producing area of the Fort Worth basin by mapping log-derived thickness, porosity, and porosity-feet. In the Barnett Shale, the density porosity (DPHI) log curve is a very useful tool to quantitatively assess shale gas resources, and gamma-ray (GR) and neutron porosity log curves are important factors in identifying the shale gas reservoir. The key data were digital logs from 146 wells selected based on the availability of GR and density log curves, log quality, and good spatial distribution. The Barnett Shale pay zone was determined on the basis of (1) DPHI >5%, (2) high GR values (commonly >∼90 API units), (3) no significant intercalated carbonate-rich beds, and (4) individual pay zones being thick enough to be commercially successful for the current design of horizontal wells. In the study area, the Barnett Shale pay zone varies from about 165 ft (50 m) to 420 ft (128 m) in thickness (H). Average DPHI values of individual wells for the pay zone vary from 8.5 to 14.0%. Porosity-feet maps of the pay zone show that areas of high DPHI-H values coincide with areas of high natural gas production, indicating that log-derived porosity-feet maps are a good method for evaluating reservoir quality and assessing natural gas resource in the Barnett Shale play. A limitation to this method is shown in the northwestern corner of the study area, which is located in the liquids-rich window with lower thermal maturity.

Finding the gas hydrate using wire- line logs

Finding the gas hydrate formation using wire line logs such as Gamma ray log, Density log Neutron porosity log, Resistivity log, by presence of GH constituents, like wise following classical methodology litho logy identification by using wire line logs, how the various logs responds to the concerned litho logy by presence of their constituents .It is to prove how the various wire line logs responds to the GH constituents

Assessment of Similarity Between Well Logs Using Synchronization Measures

In oil exploration, studying the similarity between patterns of the same geophysical properties in different wells is essential for making early decisions on future planning as well as for assessing the lithology of the area under survey. Geoscientists either rely on visual tools or resort to correlation studies between the different wells to match portions of the well logs. This is a tedious process involving several trial and error runs, which includes shifting, stretching, and sometimes preprocessing of the well logs by experienced geoscientists. However, this can be simplified by automating the process of matching. The well logs, a measure of the lithology, fall under the class of nonlinear signals. Therefore, linear methods are inadequate for matching these sequences. In this letter, we introduce similarity measures based on the concept of synchronization as used in matching nonlinear signals such as chaotic time series data. Two recently proposed methods, i.e., synchronization likelihood (SL) and visibility graph similarity (VGS), have been applied on the gamma-ray and porosity logs along different wells. These are considered as depth sequences, which can also be converted to suitable time series with the availability of the velocity profile. The data for this study originate from 12 existing wells in the western coast of India. The values of SL and VGS as well as the correlation are computed between these wells. Higher values indicate the existence of similarities. This has also been verified from the overlapped plots of well-log data.

Well Recovery, Drainage Area, and Future Drill-Well Inventory: Empirical Study of the Barnett Shale Gas Play

Summary The work is focused on one of the oldest and largest developed shale-gas plays—the Barnett shale. The primary objective is to bring more clarity and granularity to the Barnett resource estimation. The second task is to develop a resource-estimation methodology that one can use to evaluate other shale-gas plays in an attempt to determine their potential for development. We compile a comprehensive database, including production histories of more than 16,000 drilled wells and calibrated density log porosity and net-pay-zone-thickness maps. The data are used to estimate (1) the remaining technically recoverable free-gas resource, (2) individual-well recoveries, (3) areas drained by the existing wells, and (4) the future drill-well inventory. On the basis of the geographical distribution of individual-well estimated ultimate recoveries, we divide the Barnett acreage into 10 tiers. We describe the future drill-well inventory for each tier. In addition, to assess the potential contribution of refracturing to the future Barnett production, we develop a statistical procedure to identify wells that are likely to be refractured and then analyze their performances.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

Pre-Stack Seismic Inversion and Amplitude Versus Angle Modeling Reduces the Risk in Hydrocarbon Prospect Evaluation

Pre-Stack seismic inversion and Amplitude versus Angle (AVA) techniques were used in for direct hydrocarbon identification (DHI) and to understand better the risk during hydrocarbon prospecting. In order to understand and predict the seismic response for different fluid type and lithologies pre-stack seismic inversion and AVA modeling based on existing well logs information were performed. An optimized seismic inversion workflow that includes conditioning of seismic data followed by seismic petrophysics and rock physics modeling in combination with fluid substituted logs was used to predict the seismic response for different fluid types. Inverted rock properties of a wedge model were then correlated with seismic response for DHI. Another hydrocarbon prospect was studied based on AVA modeling response. AVA effects on angle gathers provide basic information on the lithology and pore fill contents of the rocks under investigation. To perform the AVA modeling, a series of forward models in association with rock physics-modeled fluid-substituted logs have been developed and associated seismic responses for different pore fluids and rock types studied. The results reveal that synthetic seismic responses together with the AVA analysis show changes for different lithologies. AVA attributes analysis show trends in generated synthetic seismic responses for different fluid-substituted and porosity logs. Reservoir modeling and fluid substitution increases understanding of the observed seismic response. It ultimately leads to a better reservoir prediction with delineation of sweet spots and improved volumetric prognosis. Assessing the effect of fluid-substituted logs for different lithologies and associated AVA seismic response can improve the prediction in reservoir characterization. Truly integrated studies of seismic, geology and well data will reduce the drilling and development risks even further. Key Words: Pre-stack seismic inversion; Wedge model; Amplitude versus angle (AVA); Fluid substitution; Intercept (I); Gradient (G); Rock physics; Derisking and forward modeling