Neutron Porosity Logs Research Articles

Acoustic televiewer amplitude data for porosity estimation with application to porewater conversion

The ability to detect and quantify rock matrix porosity is explored using acoustic televiewer images and dolostone rock core samples. In particular, an empirical relationship between the acoustic televiewer amplitude response and core-derived total porosity is developed. The performance of the acoustic televiewer amplitude relationship was compared to neutron and gamma-gamma logs, which are traditional nuclear wireline tools commonly used for estimating porosity in dolostones. Acoustic televiewer amplitude results are consistent with the gamma-gamma (density) and neutron (porosity) logs, provide increased vertical resolution, do not require downhole radioactive sources and also provide oriented fracture information. Sample specific porosity and bulk density values from the calibrated acoustic televiewer datasets are then used to improve porewater concentration estimates from rock concentrations.

Acoustic televiewer amplitude data for porosity estimation with application to porewater conversion

Abstract The ability to detect and quantify rock matrix porosity is explored using acoustic televiewer images and dolostone rock core samples. In particular, an empirical relationship between the acoustic televiewer amplitude response and core-derived total porosity is developed. The performance of the acoustic televiewer amplitude relationship was compared to neutron and gamma-gamma logs, which are traditional nuclear wireline tools commonly used for estimating porosity in dolostones. Acoustic televiewer amplitude results are consistent with the gamma-gamma (density) and neutron (porosity) logs, provide increased vertical resolution, do not require downhole radioactive sources and also provide oriented fracture information. Sample specific porosity and bulk density values from the calibrated acoustic televiewer datasets are then used to improve porewater concentration estimates from rock concentrations.

The integration of elastic wave properties and machine learning for the distribution of petrophysical properties in reservoir modeling

Conventional reservoir modeling employs variograms to predict the spatial distribution of petrophysical properties. This study aims to improve property distribution by incorporating elastic wave properties. In this study, elastic wave properties obtained from seismic inversion are used as input for an artificial neural network to predict neutron porosity in between well locations. The method employed in this study is supervised learning based on available well logs. This method converts every seismic trace into a pseudo-well log, hence reducing the uncertainty between well locations. By incorporating the seismic response, the reliance on geostatistical methods such as variograms for the distribution of petrophysical properties is reduced drastically. The results of the artificial neural network show good correlation with the neutron porosity log which gives confidence for spatial prediction in areas where well logs are not available.

Rock typing in the Upper Devonian-Lower Mississippian Woodford Shale Formation, Oklahoma, USA

Most U.S. shale plays are spatially extensive with regions of different thermal maturity and varying production prospects. With increasing understanding of the heterogeneity, microstructure, and anisotropy of shales, efforts are now directed to identifying the sweet spots and optimum completion zones in any shale play. Rock typing is a step in this direction. We have developed an integrated workflow for rock typing using laboratory-petrophysical measurements on core samples and well logs. A total of seven wells with core data were considered for rock typing in the Woodford Shale. The integrated workflow has been applied in the Woodford Shale in a series of steps. In the first step, unsupervised clustering algorithms such as [Formula: see text]-means and self-organizing maps were used to define the rock types. Rock type 1 is generally characterized by high porosity and total organic carbon (TOC). Rock type 2 had intermediate values of porosity and TOC and thus, moderate source potential and storage. Rock type 3 had the highest carbonate content, poor storage, and source rock potential. In the next step, a classification algorithm, support vector machines (SVM), was used to extend the rock types from the cores to the logs. A logging suite with gamma ray, resistivity, neutron porosity, and density logs was used for extending the rock types. These logs were used because they are commonly available and adequate to differentiate different rock types. The rock types were populated in the uncored sections of the seven cored wells and additionally in 12 wells (taken from Drilling Info) using a trained SVM model. Additional wells were taken to have sufficient data for production correlation. In the final step, a rock-type ratio (RTR) was defined based on the fraction of rock type 1 over the gross thickness. RTR was found to positively correlate with normalized oil equivalent production.

A new method for calculating gas saturation of low-resistivity shale gas reservoirs

The Jiaoshiba shale gas field is located in the Fuling area of the Sichuan Basin, with the Upper Ordovician Wufeng–Lower Silurian Longmaxi Fm as the pay zone. At the bottom of the pay zone, a high-quality shale gas reservoir about 20 m thick is generally developed with high organic contents and gas abundance, but its resistivity is relatively low. Accordingly, the gas saturation calculated by formulas (e.g. Archie) using electric logging data is often much lower than the experiment-derived value. In this paper, a new method was presented for calculating gas saturation more accurately based on non-electric logging data. Firstly, the causes for the low resistivity of shale gas reservoirs in this area were analyzed. Then, the limitation of traditional methods for calculating gas saturation based on electric logging data was diagnosed, and the feasibility of the neutron–density porosity overlay method was illustrated. According to the response characteristics of neutron, density and other porosity logging in shale gas reservoirs, a model for calculating gas saturation of shale gas was established by core experimental calibration based on the density logging value, the density porosity and the difference between density porosity and neutron porosity, by means of multiple methods (e.g. the dual-porosity overlay method by optimizing the best overlay coefficient). This new method avoids the effect of low resistivity, and thus can provide normal calculated gas saturation of high-quality shale gas reservoirs. It works well in practical application. This new method provides a technical support for the calculation of shale gas reserves in this area.

Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

Modeling and classification of the subsurface lithology is very important to understand the evolution of the earth system. However, precise classification and mapping of lithology using a single framework are difficult due to the complexity and the nonlinearity of the problem driven by limited core sample information. Here, we implement a joint approach by combining the unsupervised and the supervised methods in a single framework for better classification and mapping of rock types. In the unsupervised method, we use the principal component analysis (PCA), K-means cluster analysis (K-means), dendrogram analysis, Fuzzy C-means (FCM) cluster analysis and self-organizing map (SOM). In the supervised method, we use the Bayesian neural networks (BNN) optimized by the Hybrid Monte Carlo (HMC) (BNN-HMC) and the scaled conjugate gradient (SCG) (BNN-SCG) techniques. We use P-wave velocity, density, neutron porosity, resistivity and gamma ray logs of the well U1343E of the Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. While the SOM algorithm allows us to visualize the clustering results in spatial domain, the combined classification schemes (supervised and unsupervised) uncover the different patterns of lithology such of as clayey-silt, diatom-silt and silty-clay from an un-cored section of the drilled hole. In addition, the BNN approach is capable of estimating uncertainty in the predictive modeling of three types of rocks over the entire lithology section at site U1343. Alternate succession of clayey-silt, diatom-silt and silty-clay may be representative of crustal inhomogeneity in general and thus could be a basis for detail study related to the productivity of methane gas in the oceans worldwide. Moreover, at the 530 m depth down below seafloor (DSF), the transition from Pliocene to Pleistocene could be linked to lithological alternation between the clayey-silt and the diatom-silt. The present results could provide the basis for the detailed study to get deeper insight into the Bering Sea’ sediment deposition and sequence.

Unexpected behavior of logging-while-drilling neutron porosity logs in water-wet, sand-shale sequences

When logging through certain water-wet sand-shale sequences offshore Malaysia, the thermal neutron porosity measurement from an 8-in logging-while-drilling (LWD) tool exhibits an apparent lack of dynamic range in its sandstone porosity response. Petrophysicists using these data to determine sandstone-silt-shale points were finding that the shale points fell too near the sandstone line to be usable. An extensive nuclear Monte Carlo modeling campaign was performed to address this issue, which indicated that the tool response, while unexpected, was accurate. Modeling verified that neutron logs can exhibit a poor dynamic range just by happenstance, but an accurate assessment of the shale volume fraction [Formula: see text] should still be feasible. Surprisingly, the shale responses for this LWD tool, a smaller LWD tool, and a wireline tool are quite similar, invalidating a common expectation that LWD tools should be noticeably more epithermal in their response, and thus be less sensitive to absorbers in the clays. Modeling the actual shales, based on X-ray diffraction (XRD) analyses of cuttings, revealed the answer. In the wells with a poor dynamic range in the shale response, the quartz content was very high. Simulating the actual XRD-determined shales reproduced the actual log crossplot very nicely. Points in the shale crossplot fall near the sandstone line because the formations are primarily [Formula: see text], with only a small amount of clay. This result explains the response of the LWD tool.

A New Technique To Characterize Fracture Density by Use of Neutron Porosity Logs Enhanced by Electrically Transported Contrast Agents

SummaryFracture-density evaluation has always been challenging for the petroleum industry, although it is a required characteristic for reliable reservoir characterization. Production can be directly controlled by fracture density, especially in tight reservoirs. Previous publications showed that use of high thermal neutron-capture cross-sectional (HTNCC) contrast agents can enhance the sensitivity of neutron logs to the presence of fractures. However, all these studies focus on locating the proppants. In this paper, we introduce a method of injecting electrically transported charged boron carbide (B4C) contrast agents to naturally fractured formations to enhance the propagation of the contrast agents into the secondary-fracture (natural and induced) network by use of an externally applied electric field and to characterize the fracture density in the unpropped region by use of the enhanced neutron porosity logs.We perform numerical simulations to validate the feasibility of the proposed technique. A physical model derived from electrophoretic velocity and material-balance formulations is proposed and solved to simulate the spatial distribution of contrast agents. Furthermore, we simulate neutron porosity logs by solving the neutron-diffusion equation, which allows a fast analysis for the proposed technique.The simulation results confirmed that an external electric field can significantly enhance the transport of charged contrast agents into the secondary-fracture network. Sensitivity analysis revealed that increasing particle ζ-potential can efficiently decrease the transport time. Furthermore, we applied the introduced technique on synthetic cases with variable secondary-fracture density ranging from 1 to 8%. The relative variation in the simulated neutron porosity before and after applying the electric potential field was up to 50% in a formation with 8% fracture density after applying an electric field for 6 hours. The proposed technique can potentially enable application of neutron porosity logs in fracture characterization, including assessment of secondary-fracture density, if combined with other well logs.

New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network

Permeability is an important parameter for oil and gas reservoir characterization. Permeability can be traditionally determined by well testing and core analysis. These conventional methods are very expensive and time-consuming. Permeability estimation in heterogeneous carbonate reservoirs is a challenge task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. Permeability prediction based on well logs using artificial intelligent techniques was presented by many authors. They used several wire-line logs such as gamma ray, neutron porosity, bulk density, resistivity, sonic, spontaneous potential, hole size, depths, and other logs. The objective of this paper is to develop an artificial neural network (ANN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely resistivity, bulk density, and neutron porosity. In addition to the ANN model, in this paper and for the first time a mathematical equation from the ANN model will be extracted that can be used for permeability prediction for any data set without the need for the ANN model. Also, in this study and for the first time we introduced a new term which is the mobility index that can be used effectively in the permeability prediction. Mobility index term is derived from the mobile oil saturation that occurred due to the drilling fluid filtrate invasion. The obtained results showed that ANN model gave a comparable results with support vector machine and adaptive neuro-fuzzy inference system model. The developed mathematical equation from ANN model can be used to estimate the permeability for heterogamous carbonate reservoir based only on three parameters: bulk density, neutron porosity, and mobility index. Actual core data points (1223 points) with the three logs were used to train (857 data points, 70% of the data) and test the model for unseen data (366 data points, 30% of the data). The correlation coefficient for training and testing was 0.95, and the root-mean-square error was 0.28. The developed mathematical equation will help the engineers to save time and predict the permeability with a high accuracy using inexpensive technique. Introducing the new parameter, mobility index, in the prediction process greatly improved the permeability prediction from the log data compared to the actual measured data.

Joint petrophysical interpretation of multidetector nuclear measurements in the presence of invasion, shoulder-bed, and well-deviation effects

Recent advances in logging-while-drilling sigma measurements include three-detector thermal-neutron and gamma-ray decay measurements with different radial sensitivities to assess the presence of invasion. We have developed an inversion-based work flow for the joint interpretation of multidetector neutron, density, and sigma logs to reduce invasion, shoulder-bed, and well-deviation effects in the estimation of porosity, water saturation, and hydrocarbon type, whenever the invasion is shallow. The procedure begins with a correction for matrix and fluid effects on neutron and density-porosity logs to estimate porosity. Multidetector time decays are then used to assess the radial length of the invasion and estimate the virgin-zone sigma while simultaneously reducing shoulder-bed and well-deviation effects. Density and neutron porosity logs are corrected for invasion and shoulder-bed effects using two-detector density and neutron measurements with the output from the time-decay (sigma) inversion. The final step invokes a nuclear solver in which corrected sigma, inverse of migration length, and density in the virgin zone are used to estimate water saturation and fluid type. The fluid type is assessed with a flash calculation and Schlumberger’s Nuclear Parameter calculation code to account for the nuclear properties of different types of hydrocarbon and water as a function of pressure, temperature, and salinity. Results indicate that accounting for invasion effects is necessary when using density and neutron logs for petrophysical interpretation beyond the calculation of total porosity. Synthetic and field examples indicate that the mitigation of invasion effects becomes important in the case of salty mud filtrate invading gas-bearing formations. The advantage of the developed inversion-based interpretation method is its ability to estimate layer-by-layer petrophysical, compositional, and fluid properties that honor multiple nuclear measurements, their tool physics, and their associated borehole geometrical and environmental effects.

Present Gas Reserve Estimation Using Wireline Logging Data of Habiganj Gas Field, Bangladesh

Habiganj Gas Field (HGF) is one of the most producing and promising gas field in Bangladesh which is ranked as second largest gas field in aspects of Gas Initially In Place (GIIP). It formed by a north south elongated anticline which lies in the northernmost end of the 130km long Baramura anticline from Tripura (India) in the south. This paper shows the present gas reserve estimation with a view of four gas producing wells (i.e. HBJ-7, HBJ-9, HBJ-10, and HBJ-11) in account of upper gas sand has been access via the investigation of lithology, wire line logging data and production data are used as parameter for volumetric method. The average water and gas saturation of the reservoir has also been determined by Archie equation. From the previous analysis with logging data of HGF, it was found that the Gas Water Contact (GWC) was at 1458m and at present it is at about 1415m. The upper gas sand lies at a depth of 1320m below the surface and has a maximum gross pay 230m thick. The gas sand shows average porosity 30% (calculated from the combination of neutron and density porosity logs) and average permeability in the range of 2-4 Darcy’s which indicates an excellent quality reservoir. The water saturation of Habiganj gas sand layer is considered for the depth range of 1330m to 1510m with 5m interval in-deed. Thereafter, water saturation and gas saturation of those wells (HBJ-7, HBJ-9, HBJ-10 and HBJ-11) are 27% and 73%, 29% and 71%, 15% and 85%, and 19% and 81% respectively. The gas formation volume factor (Bg) is estimated at about 0.0075 (rcf/scf) for the average 2053.13psi shut in pressure. From the above discussion it is found that the Gas Initially in Place by HBJ-7, HBJ-9, HBJ-10, and HBJ-11 wells are 5.35Tcf, 4.83Tcf, 5.71Tcf and 5.20Tcf respectively and the cumulative reserve for the HGF is 5.25Tcf while the recoverable reserve is 3.13Tcf. This study would be significant implications for reserve estimation and decision making in an effort to explore the present condition of gas reserve in HGF and the techniques described here can be used in other gas field as a base line for projections of gas reserve.

Electric-Hydraulic Correlations in Layered Aquifers: A Case Study of the Surficial Aquifer of Emirate Abu Dhabi, United Arab Emirates

Longitudinal conductance and transverse resistance are bulk electrical properties of a geoelectric formation and can be correlated with the bulk transmissivity of an aquifer within the formation. In aquifers that are electrically-resistive relative to adjacent layers in a horizontally layered Earth, bulk transverse resistance has been shown to correlate with aquifer transmisivity. Conversely, in aquifers that are electrically-conductive relative to adjacent layers, a correlation between bulk longitudinal conductance and aquifer transmissivity has been demonstrated. In both cases, previous investigations have relied on small datasets that have yielded coefficients of determination (R2) that are typically in the range of 0.6 to 0.7 to substantiate these relations. This paper explores electric-hydraulic relations of the surficial aquifer of Emirate Abu Dhabi, United Arab Emirates, by using a larger dataset compared to previous studies of these relations. Geophysical logs from 26 wells are used to correlate the bulk electrical properties of the aquifer, which are computed from deep electromagnetic (EM) induction resistivity logs, to the bulk transmissivity of the surficial aquifer, which is modeled from neutron porosity logs. The bulk longitudinal conductance was highly correlated (R2 = 0.8387) with the bulk transmissivity of the surficial aquifer. The bulk Dar-Zarrouk resistivity is negatively correlated with the aquifer transmissivity; an R2 value of −0.5604 is obtained.

A method to improve the sensitivity of neutron porosity measurement based on D-T source

Compensated porosity logging tool utilizing deuterium-tritium (D-T) source shows a lower sensitivity to the variation of formation porosity compared with that adopting Am-Be source. In order to improve the sensitivity, the factors of an infinite homogeneous formation influencing slowing-down length and the near to far counts ratio are analyzed. Then Monte Carlo simulation method is used to build well logging models to study the responses of a neutron porosity logging tool to hydrogen index and formation density. It shows that in addition to hydrogen index, the variation of the density also has a great impact on slowing-down length and the ratio which reduces the response sensitivity to porosity. A new model depicts the relationship between the count ratio and porosity is proposed. When the model is used to process the measured ratio, the ratio shows improved dynamic range and sensitivity to porosity compared with the values without processing.

PRELIMINARY ESTIMATE OF CO2 STORAGE CAPACITY BY PETROPHYSICAL MODELLING IN UPPER MIOCENE POLJANA SANDSTONES IN THE WESTERN PART OF THE SAVA DEPRESSION

A preliminary assessment of the capacity of regional geological storage in the western part of the Sava Depression was based on data obtained from several deep exploration wells. The Poljana Sandstones represent a regional deep sandstone body, in most parts saturated with water, with promising underground facilities for the storage of CO₂ in the study area. Poljana Sandstones (member of Klostar-Ivanic Formation bounded between E-log markers Rνand Z') have favourable petrophysical properties and are situated at reasonable depths. According to previous investigations, at depths greater than 800 meters supercritical conditions of temperature and pressure CO₂ are achieved, which ensures easy and safe injection into storage underground facilities. For the creation of a model in Petrel software, various data was used, including the distribution of CO₂ density, porosity, effective thickness and the relative depth of sandstone. Spatial distribution of porosity was made regarding neutron porosity logs. The most important parameter in the estimate of storage capacity is effective thickness, defined by the interval between E–log markers Rν and Z’. Hence, the effective thickness was used for top and bottom surface of sandstones. Density of CO₂ was created according to their spatial distribution regarding the depth and the temperature. The capacity of CO2 storage was calculated by the volumetric method. The use of a calculated Petrel model can subsequently determine the amount of CO₂ storage in the underground facilities of the study area.

New Analytical Method To Calculate Matrix- and Fluid-Corrected Total Porosity in Organic Shale

Summary Estimation of total porosity from neutron and density porosity logs in organic shale (source rock) is challenging because these logs are substantially affected by fluid and matrix-composition effects. Conventional interpretation of neutron and density porosity logs often includes corrections for shale concentration in which the main objective is to improve the calculation of nonshale porosity in hydrocarbon-bearing zones. These corrections are not desirable in unconventional rock formations because shale pores can be hydrocarbon-saturated. Neutron and density porosity readings across shale zones are sometimes averaged by use of the root-mean-square (RMS) method. We introduce a new and simple analytical expression for total porosity that effectively separates both matrix and fluid effects on neutron and density porosity logs. The expression stems from a new nonlinear mixing law for neutron migration length that is coupled with the linear-density mixing law to calculate total porosity and fluid density. The method is applied in two sequential steps: First, separate corrections for only matrix effects are implemented to enhance the neutron-density crossover for qualitative interpretation of fluid type; second, the coupled equation is used to estimate fluid density and actual porosity devoid of matrix and fluid effects. Calculated porosity and fluid density can be used further to calculate water saturation from density logs. One remarkable feature of this method is the ease with which it can be applied to obtain accurate and reliable results. Application of the method only requires knowledge of single-component nuclear properties and mineral volumetric concentrations. One can obtain nuclear properties from a set of charts for multiple fluid types and minerals provided in this paper, whereas one can calculate mineral concentrations on the basis of available triple combo logs or gamma ray spectroscopy logs. Two synthetic and four field examples (two conventional and two shale-gas reservoirs) are used to test the method. First, we describe an application in a conventional siliciclastic sedimentary sequence in which only shale concentration calculated from gamma ray logs is required to improve the estimation of porosity in shaly sections. Second, we document several applications in which gamma ray spectroscopy logs are used together with a reliable hypothesis for clay type to define mineral properties. Results compare well with nuclear-magnetic-resonance (NMR) and core measurements, whereas the new method outperforms the conventional RMS procedure, especially in the cases of gas-bearing, low-porosity organic shale. The new analytical method can be readily implemented on an Excel spreadsheet and requires minimal adjustments for its operation.

Reservoir characterization using multifractal detrended fluctuation analysis of geophysical well-log data

The spatio-temporal variations in geophysical well-log signals, which often reflect their scale invariant properties, can be well studied with multifractal analysis. In this study, we have carried out fractal and multifractal studies using detrended fluctuation analysis (DFA) and multifractal DFA (MFDFA) respectively. While the DFA primarily facilitates to understand the intrinsic self-similarities in non-stationary signals like well-logs by determining the fractal scaling exponents in a modified least-squares sense, the MFDFA, which in fact, is a generalization of DFA, provides a comprehensive understanding of the multifractal behaviour of the signals through multifractal singularity spectrum as well as the Hurst exponents. DFA and MFDFA have been applied to gamma-ray log and neutron porosity logs of two wells (well B and well C), located in the western offshore basin, India, to study the nature of the subsurface formation properties, vis-à-vis their multifractal behaviour. The estimated DFA fractal scaling exponents, represented in the form of contour plots enable easy identification of the depths to the tops of reservoir zones. On the other hand, the multifractal singularity spectra provide a unique platform for an improved interpretation of logs in terms of their sedimentation pattern and lithological differences. This has been tested with gamma-ray log data of wells B and C. We show that the multifractal behaviour of gamma-ray log is largely influenced by the presence of shale and variations in the subsurface sedimentation pattern. Similarly, the role of gas in a pay zone on the multifractal behaviour was established by comparing the multifractal singularity spectra of the original neutron porosity log and a synthetic neutron log (which we call gas-corrected log), generated using density log. The MFDFA of only that portion of the original neutron log representing the pay zone and its gas-corrected equivalent unequivocally suggest that the presence of gas in the reservoir zones weakens the multifractal behaviour of neutron porosity logs. This emphasizes the significance of multifractal studies of well-logs for effective reservoir characterization. The observed multifractal behaviour in all logs is found to be due to the presence of long-range correlations in the 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.

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.

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

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.