Neutron Porosity Research Articles

Cross plotting of Rock Properties for Fluid and Lithology Discrimination using Well Data in a Niger Delta Oil Field

The cross plotting of rock properties for fluid and lithology discrimination was carried out in a Niger Delta oil field using well data from a given oil field. The data used for the analysis consist of suites of six wells. The reservoir was evaluated using gamma ray logs, volume of shale, resistivity and neutron porosity for well-02 and well-06. Sand lithology showed low gamma ray, high resistivity and low acoustic impedance. Cross plotting was carried out and the plots with most outstanding results were Vp/Vs versus AI, λρ Versus Vp/Vs, μρ against Density and λρ versus μρ. For the two wells considered, two reservoirs were observed, one reservoir was observed in well - 02 at a depth of 4600 ft to 5050 ft .and a reservoir was also observed for well - 06 at a depth of 10240 ft to 10600 ft. Crossplotting of rock properties discriminated the wells into lithologies of sand and shale which is typical of Niger Delta. The crossplot also discriminated the fluids into gas, brine and oil. This study has been able to discriminate hydrocarbon reservoirs using well logs in the field of study. Keywords : Hydrocarbon, Reservoir, Well – Logs, Crossplotting, Lithology

Inversion of well logs into facies accounting for spatial dependencies and convolution effects

Abstract We predict facies from wireline well log data for a fluvial deposit system offshore Norway. The wireline well logs used are sonic, gamma ray, neutron porosity, bulk density and resistivity. We solve this inverse problem in a predictive Bayesian setting, and perform the associated model parameter estimation. Spatial vertical structure of the facies is included in the model by a Markov chain assumption, making geological model interpretation possible. We also take convolution effect into account, assuming that the observed logs might be measured as a weighted sum of properties over a facies interval. We apply the methods on real well data, with thick facies layers inferred from core samples. The proposed facies classification model is compared to a naive Bayesian classifier, which does not take into account neither vertical spatial dependency, dependencies between the wireline well logs nor convolution effect. Results from a blind well indicate that facies predictions from our model are more reliable than predictions from the naive model in terms of correct facies classification and predicted layer thickness.

Using ANFIS and Neural Networks to Predict the Volume Percentage of Matrix and Fluid

In the common methods to find percentage of lithology and fluids, well log information, petrophysical tests, and high-skill experts are needed. Therefore, designing a model that is able to evaluate the percentage of lithology using well log data without laboratory information will be very economical. Today, by using artificial neural networks, lithology can be predicted very well from the lowest information and time. Various artificial neuro-fuzzy inference systems and radial basis function and back propagation neural network were implied (about 1,500 accredited data and evaluated lithology and fluid volume percentages were available). In this article, first several kinds of features tested by artificial neuro-fuzzy inference systems model and finally neutron porosity (NPHI), conductive log (CT), resistivity log (RT), density (RHOB), natural gamma (GR), and photo electron (PE) logs were determined as the best logs to predict the percentage of lithology and fluid’s volume. Then these logs were used as input variable for radial basis function and back propagation neural network. In the second optimization, several training functions were implemented to select the best structure by lowest error. Comparison between targets and a network’s output data shows that neural networks are suitable tools for predicting the percentage of lithology and fluid’s volume, consuming less time and reducing cost.

Magnetotelluric imaging integrated with seismic, gravity, magnetic and well-log data for basement and carbonate reservoir mapping in the São Francisco Basin, Brazil

We evaluate the use of the magnetotelluric (MT) method to locate crystalline basement and overlying carbonate reservoir rocks underneath a thick overburden in the São Francisco basin in Brazil. Mapping the complex basement and the carbonate reservoir using seismic reflection is a major problem in hydrocarbon exploration in this intracratonic basin, and it is expected that MT will provide useful complementary information. In the present study, we analysed 31 MT soundings along four survey lines in the central region of the basin. The MT soundings covered a period range of 0.001–100 s, probing the subsurface resistivity structure down to a maximum depth of about 15 km. The MT data were inverted using a regularized two-dimensional (2D) inversion algorithm with a variety of a priori data for comparison. For model appraisal, we analysed well log (gamma ray, deep resistivity and neutron porosity) data as well as seismic, gravity and magnetic profiles coincident with one MT line passing through the well. We found that shallow geological boundaries separating zones of strong resistivity contrasts also coincide with seismic boundaries in the inversion models with or without a priori data. Using gravity data, it was also possible to define the compartmentalized basement in this sector of São Francisco Basin, not clear in the seismic section. However, only by integrating all available information were we able to map the Lagoa do Jacaré and Sete Lagoas carbonate member-formations of the Bambuí Group, which are considered to host both the source and reservoir rocks identified from past exploratory history of this basin. We also imaged a basement structural high with thinned or disrupted conductive cover rocks over a known zone of hydrocarbon microseepage and a buried conductive (source rock?) channel at its NW margin, the trace of which coincides with the present-day River Paracatu along which gas bubbles have been observed. This suggests that MT may be fruitfully integrated with gravity, magnetic and seismic data to study the structural controls on hydrocarbon occurrence in this basin.

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.

Technology Focus: Formation Evaluation (August 2015)

Technology Focus Comprehensive formation evaluation is performed by integrating logs, cores, well tests, and other geoscience and engineering information. It plays a critical role in resources assessment and is equally important in well-placement development and reservoir surveillance. It is one of the technical focus areas that is fundamental across almost all upstream businesses, from initial exploration through final field abandonment. So how well are we doing in modern formation evaluation? In resources exploration, we have come a long way from the historical wireline (WL) triple combination of logging of bulk density, neutron porosity, and resistivity for reservoir lithology, porosity, and saturation, respectively. With continuing development in technologies such as acoustic, mud logging, nuclear magnetic resonance, imaging, and formation testing and sampling, we can identify fluid types and contacts; quantify viscosity, free fluid, and mobility; and characterize pore structure and rock mechanical properties. In addition, elemental mineralogy logs have significantly improved multimineral formation evaluation in complex reservoirs, especially unconventional shale formations. In field development, a paradigm shift has occurred in the last decade in terms of wells drilled (i.e., we are drilling more and more horizontal, multilateral wells for maximum/extreme reservoir contact). Consequently, logging-while-drilling technologies have advanced so much that they can provide almost WL-log quality and capability. To achieve the ultimate goal of maximizing reservoir performance, looking-around and -ahead technologies are also evolving quickly. In reservoir surveillance, game-changing technologies are emerging that enable us to illuminate the reservoir in four dimensions through measurements across wells, from wellbore to surface, and monitor smart-nanoparticle movement in situ. Development of these reservoir-scale technologies is actually in line with the grand challenges facing the exploration-and-production industry identified by SPE (i.e., developing higher-resolution subsurface imaging and increasing hydrocarbon recovery factor) because they are critical for improvements in identifying bypassed oil and assessing remaining oil. Core analysis has historically been considered as a reference for log interpretation. Major developments in core analysis in the last decade are digital rock physics and unconventional-resources evaluation. These developments continue to push the envelope of our understanding of rock and of rock/fluid interactions. In summary, significant progress has been made recently in formation evaluation, and we are in the process of a great journey to help resolve the grand challenges with fit-for-purpose and cost-effective solutions, as demonstrated in the selected papers and recommended additional reading. JPT

New scaling model for variables and increments with heavy‐tailed distributions

AbstractMany hydrological (as well as diverse earth, environmental, ecological, biological, physical, social, financial and other) variables, Y, exhibit frequency distributions that are difficult to reconcile with those of their spatial or temporal increments, ΔY. Whereas distributions of Y (or its logarithm) are at times slightly asymmetric with relatively mild peaks and tails, those of ΔY tend to be symmetric with peaks that grow sharper, and tails that become heavier, as the separation distance (lag) between pairs of Y values decreases. No statistical model known to us captures these behaviors of Y and ΔY in a unified and consistent manner. We propose a new, generalized sub‐Gaussian model that does so. We derive analytical expressions for probability distribution functions (pdfs) of Y and ΔY as well as corresponding lead statistical moments. In our model the peak and tails of the ΔY pdf scale with lag in line with observed behavior. The model allows one to estimate, accurately and efficiently, all relevant parameters by analyzing jointly sample moments of Y and ΔY. We illustrate key features of our new model and method of inference on synthetically generated samples and neutron porosity data from a deep borehole.

Investigation on log responses of bulk density and thermal neutrons in coalbed with different ranks

Density and neutron logs play an important role in the exploration of coalbed methane (CBM) reservoirs. However, the study of these two log responses of coalbeds is deficient. Based on laboratory data in the published literature and field logs of CBM reservoirs from several coal basins in China, this paper focuses on acquiring and analyzing variations in bulk density and thermal neutrons of coal with different ranks. Two new methods are introduced to correct the effect of ash on field logs to obtain the log values of ash-free coal. The corrected coalbed density logs are in accordance with the density of coal samples in the laboratory. Then, hydrogen indices of coals with different ranks are simulated, and the corrected neutron logs are well verified by simulated values. After obtaining the variations of density and neutrons of coalbeds with different ranks, the behaviors of density and neutron logs are analyzed and discussed. Based on the laboratory data and the simulated and corrected field logs, the specific values of density and neutron porosity for ash-free coals with higher ranks are listed. Furthermore, a crossplot of density versus neutron logs is proposed to identify coal ranks, which has been successfully applied to various CBM fields of several basins in China.

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.

Uncertainty in geosteering and interpretation of horizontal wells — The necessity for constraints and geometric models

While geosteering or interpreting horizontal wells, engineers are constantly faced with the challenge of correctly defining the well position with respect to the target reservoir and other geologic markers from an often incomplete set of data. Traditional petrophysical evaluation can be performed only after the geometric relationship between the well and the target reservoir is interpreted. Measurements made while drilling range from a simple gamma-ray (GR) log to resistivity, apparent density and neutron porosities, borehole images, azimuthal deep resistivity, and beyond. With one or a combination of these logs and other peripheral data such as well logs from offset/pilot wells and seismic images, the question remains as to how accurate the interpreted relationship is between the well trajectory and the target reservoir. In a vertical well, an explicit geometric model of the formation is often not necessary. There is a 1D layered-earth model, either consciously or subconsciously, in people's minds to aid the interpretation. On the other hand, because of the lateral variation of properties and thus the geologic complexity encountered in a horizontal well, it becomes critical to explicitly construct a formation cross-sectional model, validated in terms of its correctness and uniqueness against the available measurements and other known information. To properly construct, update, and validate the formation model, the completeness of information for solving the geometric relationship between the well and the target reservoir is investigated. It is found that a data set with logs from the horizontal well alone is not adequate for the task. Proper constraints based on depositional environments must be introduced. Well logs from offset/pilot wells define the formation sequence and add the high-resolution details to the interpretation. Three-dimensional reservoir models from seismic images guide the interpretation of the geologic trends along the well trajectory and the extrapolation of the model property into the volume that is not sensitive to well logs. Modeling the tool responses and understanding the underlying response characteristics help to mitigate the interpretation uncertainty by extracting more geometric information from the physical measurements. One should also be aware of the possibility that the interpretation might not be unique even though a model fits the data.

Sequence stratigraphic analysis of fluvial deposits using facies characterization and wireline log correlation: case of the late Early-early Middle Devonian Snake Cave Interval, Darling Basin, Australia

In this article, we present the application of wireline log correlation for sequence stratigraphic analysis to the late Early to early Middle Devonian Snake Cave Interval in the Neckarboo Sub-basin (DM Kewell East DDH-1 well) and Hillston Trough (DM Mossgiel DDH-1 well), Darling Basin. The results of an integrated study using characterising wireline log signatures (gamma ray, sonic, resistivity shallow, long spaced neutron and limest neutron porosity) and core description data supplemented by limited palaeontological reports indicate that three major upper Lower to lower Middle Devonian cycles have controlled sedimentation of this stratigraphic succession. We distinguished seven core sedimentary facies, which were grouped into six electrofacies defined by wireline log signatures. The electrofacies were defined by describing and analysing the graphic gamma ray logs using basic geometrical shapes, such as bell, serrated bell, funnel, serrated funnel, cylinder (block) and linear. Six electrofacies informally named electrofacies SA, SB, SC, SD, SE and SF correspond to seven core facies (MS1-MS7), which have progressively greater sandstone/shale ratios and better electrofacies quality. These electrofacies are characterised as follows: electrofacies SA corresponds to the conglomeratic braided channel-fill, electrofacies SB corresponds to channel lag and point bar, electrofacies SC corresponds to crevasse splay sandstones and flood-plain fines, electrofacies SD corresponds to multistorey fluvial channel-fills, electrofacies SE corresponds to sandy braided channel-fills and electrofacies SF corresponds to distributary channel sands and local small delta-plain. The upper Lower to lower Middle Devonian sequence (Snake Cave Interval) of the Neckarboo Sub-basin and Hillston Trough is characterised by two major third-order sequences (Snake Cave sequence 1 and Snake Cave sequence 2) that consist of ten systems tracts and ten sequence surfaces. Snake Cave sequence 1 (SCS1) consists of two cycles with progradational to retrogradational trends (LST1 to TST1 and LST2 to TST2 separated by a marine flooding surface) divided into five sequence surfaces (SBSC1, TS1 MFS1, TS2 and MFS2, in ascending order) as defined by wireline log signatures. One cycle has progradational, retrogradational to aggradational trends (LST3, TST3 to HST1) and has seven sequence surfaces (e.g. MFS2, LST3, TS3, MFS3 and SBSC2, in ascending order). Snake Cave sequence 2 (SCS2) consists of one unit with progradational, retrogradational to aggradational trends (LST4, TST4 and HST2, in ascending order) with three sequence surfaces (e.g. SBSC2, TS4 and MFS4). No TST4 and HST packages were observed, and the MFS4 is poorly developed. The recognition of sedimentological sequence stratigraphic changes above and below marine flooding surfaces (MFS1-4), where deposition sequences have been defined without adequate biostratigraphic control, is easy since depositional sequence and bounding surface correlation can always be clearly demonstrated. A sequence stratigraphic architecture that can be used to accentuate and strengthen Darling basin exploration also documents significant changes in the local and regional tectonostratigraphic setting.

Scalable statistics of correlated random variables and extremes applied to deep borehole porosities

Abstract. We analyze scale-dependent statistics of correlated random hydrogeological variables and their extremes using neutron porosity data from six deep boreholes, in three diverse depositional environments, as example. We show that key statistics of porosity increments behave and scale in manners typical of many earth and environmental (as well as other) variables. These scaling behaviors include a tendency of increments to have symmetric, non-Gaussian frequency distributions characterized by heavy tails that decay with separation distance or lag; power-law scaling of sample structure functions (statistical moments of absolute increments) in midranges of lags; linear relationships between log structure functions of successive orders at all lags, known as extended self-similarity or ESS; and nonlinear scaling of structure function power-law exponents with function order, a phenomenon commonly attributed in the literature to multifractals. Elsewhere we proposed, explored and demonstrated a new method of geostatistical inference that captures all of these phenomena within a unified theoretical framework. The framework views data as samples from random fields constituting scale mixtures of truncated (monofractal) fractional Brownian motion (tfBm) or fractional Gaussian noise (tfGn). Important questions not addressed in previous studies concern the distribution and statistical scaling of extreme incremental values. Of special interest in hydrology (and many other areas) are statistics of absolute increments exceeding given thresholds, known as peaks over threshold or POTs. In this paper we explore the statistical scaling of data and, for the first time, corresponding POTs associated with samples from scale mixtures of tfBm or tfGn. We demonstrate that porosity data we analyze possess properties of such samples and thus follow the theory we proposed. The porosity data are of additional value in revealing a remarkable cross-over from one scaling regime to another at certain lags. The phenomena we uncover are of key importance for the analysis of fluid flow and solute as well as particulate transport in complex hydrogeologic environments.

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.

Prediction of porosity in crystalline rocks using artificial neural networks: An example from the Chinese Continental Scientific Drilling Main hole

Porosity plays an important part of understanding permeability and fluid flow within the continental, crystalline rocks. Geophysical well logs are presently the most consistent means of providing continuous information for porosity estimation. However, it is difficult to interpret geophysical well logs data in crystalline rocks due to their complex geological features and the difficulty in understanding and using the complex and intensive information content in these data. Motived by the successful prediction abilities of artificial neural networks (ANN) to solve different problems in geophysics, this study explore the applicability of using ANNs to predict porosity in continental, crystalline rocks. This ANN technique is calibrated on Chinese Continental Scientific Drilling Main Hole (CCSD-MH) data, which provides core porosity data combined with four geophysical well logs (density, neutron porosity, sonic and resistivity). The data from CCSD-MH is utilized to train feed-forward backpropagation (FFBP) neural network and radial basis function (RBF) neural network to derive a relationship between geophysical well logs and porosity, and hence predict porosity accurately. The findings demonstrate that ANNs provide better performances with sets of three geophysical well logs (density, sonic and resistivity) than regression technique. Comparison of FFBP to RBF showed that RBF reveals better stability and more accurate performances than FFBP. Based on the success achieved in this study, this intelligence artificial technique can be a very advantageous tool in facilitating the task of geophysicists in the framework of research drillings in continental crust.

The Study of Identifying Deformed Coal by Logging Curve on Coalbed Methane Development

Coalbed methane (CBM) which is a kind of efficient and clean unconventional natural gas will be the alternative energy in the future. Deformed coal which is harmful to CBM development was studied with logging curve. The logging curve characteristics and template which can be used directly had been put forward and applied in Shizhuang Block, Qinshui basin, China: (1) Deformed coal has low resistivity, low bulk density, high interval transit time, expanding well diameter and increasing neutron porosity in logging curve. (2) 3 type coals (undeformed coal-transitional coal - deformed coal) are arranged from lower-left corner to top right corner in order in AC and LSD diagram. (3)The deformed coal proportion of No.15 coal is significantly higher and it maybe the main restricting development factor. (4) The northeast and southwest of Shizhuang block have more deformed coal.

Lacustrine depositional environments in the Green River Formation, Uinta Basin: Expression in outcrop and wireline logs

Lacustrine basins are key oil-productive areas of the world. Because fewer lacustrine exist than marine basins, lacustrine systems are relatively less well studied. This paper investigates fluvial-lacustrine depositional environments and their representation in wireline logs in the lower part of the Green River Formation, Uinta Basin, Utah. The five principal depositional environments of the lower Green River Formation are (1) deep lacustrine, (2) shallow lacustrine, (3) lacustrine delta, (4) coastal plain, and (5) alluvial plain. Deep-lake environments are characterized by laminated oil shales and fine-grained carbonates. These facies exhibit anomalously high neutron porosity, and low bulk density relative to other settings. Shallow-lake environments are dominated by weakly laminated to massive gray mudstones, and limestones, with occasional thin high-bulk-density sandstones. Lacustrine deltas (both sand-prone and mud-prone) grade from shallow-lake muds to ripple-laminated to cross-bedded sandstones. The upward decrease in clay can be seen in the gamma-ray, neutron-porosity, and bulk-density profiles of deltaic intervals. Coastal plain mudstones have a greenish hue, and frequently contain organic matter. Channels in coastal plain settings are typically thin, isolated, and heterolithic. Alluvial plain channels tend to be sandier, thicker, and less isolated than coastal plain channels. Alluvial mudstones are reddish with abundant pedogenic features. The vertical association of depositional environments in the lower Green River indicates both high-amplitude and high-frequency lake-level fluctuations. However, the macro-scale trend shows a rapid deepening of the lake lower in the section, followed by a gradual filling of the accommodation, and a gradual flooding near the top of the studied interval. The lower Green River depositional environments form key petroleum system components. Oil shales in the deep lacustrine settings are the major source rock, and coastal plain muds are a potential minor source. Regional seals are formed by deposits of tight lacustrine shale and carbonate deposited in both marginal and deep lacustrine settings. Delta, coastal plain, and alluvial plain sands form the principal reservoirs. Some deep lacustrine mudstones and carbonates are also potential unconventional reservoirs. Correlation of outcrop observations from well log expressions allows depositional environments to be interpreted in the Uinta Basin and other lacustrine basins.

Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oil field, Algeria

Abstract Magnetic susceptibility (Ms) measurements were carried out in the cores of six wells distributed throughout the study area of tight sand oil of the Hamra quartzites reservoir, southwest of the Hassi Messaoud oil field. With steps of 2 cm, 8760 Ms values were taken from a total core length of 174 m. In most of the intervals, the contrasts in Ms coincide with high fracture density and high shale content (increase of gamma ray in this interval). The analysis of the hysteresis loops, isothermal remanent magnetizations to saturation (IRMs) and thermomagnetic curves for samples taken from some high interval of Ms shows that magnetite and pyrrhotite are the main magnetic minerals. It demonstrates that fractures can be considered as precipitation environments of the magnetic minerals in the reservoir, which is a good indicator for mineralized fractures. The application of principal component analysis (PCA) to the entire reservoir shows low linear correlation between Ms and the main petrophysical parameters (gamma ray, neutron porosity, density, and saturation of oil). Meanwhile, the application of fuzzy ranking and artificial neural network (ANN) evidenced non-linear relations between these parameters. It is justified by the prediction of the Ms from the petrophysical parameters with an acceptable degree of accuracy. The results obtained using an ANN structure of 25 neurons in a hidden layer show the performance in the test stage with mean square error (MSE), mean relative error (MRE) and correlation coefficient (R) equal to 0.0142, 0.0743 and 0.907, respectively.

A comparative study of two data-driven methods in detection of vuggy zones: a case study from a carbonate reservoir, west of Iran

Vuggy porosity is a key pore space type associated with the characterization of any carbonate reservoir. This porosity has significant influence on many parameters in reservoirs such as permeability, oil recovery, pressure drop and production. In this paper, two data-driven methods (i.e., Bayesian and Parzen classifiers) are used to identify vuggy porosity zones from well logs. Four well logs namely gamma ray (GR), neutron porosity (NPHI), bulk density (RHOB) and sonic (DT) from three oil wells drilled in Sarvak Formation, west of Iran, were integrated to examine the capability of the methods. The results show that the Parzen classifier gives better results than Bayesian in terms of classification accuracy. The Parzen technique classifies the given depth with an average accuracy of 77.7 % in single-well analysis and average accuracy of 67.3 % in generalization step.

A combined Parzen-wavelet approach for detection of vuggy zones in fractured carbonate reservoirs using petrophysical logs

Abstract Vuggy porosity which is the most important type of porosity in carbonate rocks, significantly affects permeability, pressure drop and recovery factor of the rocks. As a result, its identification and modeling are essential for reservoir characterization and history matching. Image logs as well as RCAL and SCAL tests are main methods to determine vugs. However, these methods are costly and usually unavailable. Therefore, developing a generalized approach for recognizing vugs appears to be necessary. In this paper, a combined Parzen-wavelet based algorithm is developed for identifying vuggy zones in wavelet coefficient domain using gamma ray (GR), neutron porosity (NPHI), bulk density (RHOB) and sonic (DT) logs. Compatibility between core tests and the results of the developed method revealed the capability of the method.