Neutron Porosity Research Articles

Facies recognition using a smoothing process through Fast Independent Component Analysis and Discrete Cosine Transform

We propose a preprocessing methodology for well-log geophysical data based on Fast Independent Component Analysis (FastICA) and Discrete Cosine Transform (DCT), in order to improve the success rate of the K-NN automatic classifier. The K-NN have been commonly applied to facies recognition in well-log geophysical data for hydrocarbon reservoir modeling and characterization.The preprocess was made in two different levels. In the first level, a FastICA based dimenstion reduction was applied, maintaining much of the information, and its results were classified; In second level, FastICA and DCT were applied in smoothing level, where the data points are modified, so individual points have their distance reduced, keeping just the primordial information. The results were compared to identify the best classification cases. We have applied the proposed methodology to well-log data from a petroleum field of Campos Basin, Brazil. Sonic, gamma-ray, density, neutron porosity and deep induction logs were preprocessed with FastICA and DCT, and the product was classified with K-NN. The success rates in recognition were calculated by appling the method to log intervals where core data were available. The results were compared to those of automatic recognition of the original well-log data set with and without the removal of high frequency noise. We conclude that the application of the proposed methodology significantly improves the success rate of facies recognition by K-NN.

Novel Glass Ceramic Scintillator for Detection of Slow Neutrons in Well Logging Applications

One of the neutron detection techniques is based on the scintillation glasses enriched with the <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> Li isotope. A limitation to increase the light yield (LY) in glass scintillators is imposed by the absence of long-range order in their atomic structure. As a result, the mean free path of excitons in glasses is very small and the delivery of electronic excitations to luminescent centers through the exciton migration has very low efficiency. The only way toward creating effective scintillation in glasses is to use direct excitation of the luminescent centers, which are dopants in the form of Ce <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3+</sup> ions. However, the synthesis of the glasses heavily doped with cerium possesses significant technological challenge due to heterovalent properties of cerium. These limitations can be overcome by the impregnation of the glass matrix with scintillator nanocrystals with average size less than the emitted light wavelength. The controlled recrystallization of the glass enables control of the nanocrystal dimension and their distribution in the glass matrix. Details of the method are described in US patent application 13/242 839. By using this approach we have synthesized glass ceramic scintillation material obtained through partial crystallization of Ce-doped lithium-silica glass with the formation of the petalite nanocrystals in the glass matrix. Samples containing 16.5 at.% of lithium demonstrate bright radio-luminescence with maximum at 410 nm, fast scintillation with an average decay time constant of 70 ns, and LY up to 240% of LY of GS20 scintillation glass. LY of the obtained material is almost constant between 20° C and 70 °C and depends weakly on the temperature decreasing by ~30% with the temperature increase from 70 °C to 170 °C. Scintillation detectors based on this material can replace He3 neutron counters in many applications, including wireline and logging-while-drilling (LWD) “sourceless” neutron porosity measurement that requires the neutron detectors capable to operate at temperatures above 150 °C.

Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, Algeria

Fracture density estimation is an indisputable challenge in fractured reservoir characterization. Traditional techniques of fracture characterization from core data are costly, time consuming, and difficult to use for any extrapolation to non-cored wells. The aim of this paper is to construct a model able to predict fracture density from conventional well logs calibrated to core data by using artificial neural networks (ANNs). This technique was tested in the Cambro-Ordovician clastic reservoir from Mesdar oil field (Saharan platform, Algeria). For this purpose, 170 cores (2120.14m) from 17 unoriented wells have been studied in detail. Seven training algorithms and eight neuronal network architectures were tested.The best architecture is a four layered [6-16-3-1] network model with: a six-neuron input layer (Gamma ray, Sonic interval transit time, Caliper, Neutron porosity, Bulk density logs and core depth), two hidden layers; the first hidden layer has 16 neurons, the second one has three neurons. And a one-neuron output layer (fracture density). The results based on 8094 data points from 13 wells show the excellent prediction ability of the conjugate gradient descent (CGD) training algorithm (R-squared=0.812).The cross plot of measured and predicted values of fracture density shows a very high coefficient of determination of 0.848. Our studies have demonstrated a good agreement between our neural network model prediction and core fracture measurements. The results are promising and can be easily extended in other similar neighboring naturally fractured reservoirs.

Estimation of dynamic petrophysical properties of water-bearing sands invaded with oil-base mud from the interpretation of multiple borehole geophysical measurements

Nonmiscible fluid displacement without salt exchange takes place when oil-base mud (OBM) invades connate water-saturated rocks. This is a favorable condition for the estimation of dynamic petrophysical properties, including saturation-dependent capillary pressure. We developed and successfully tested a new method to estimate porosity, fluid saturation, permeability, capillary pressure, and relative permeability of water-bearing sands invaded with OBM from multiple borehole geophysical measurements. The estimation method simulates the process of mud-filtrate invasion to calculate the corresponding radial distribution of water saturation. Porosity, permeability, capillary pressure, and relative permeability are iteratively adjusted in the simulation of invasion until density, photoelectric factor, neutron porosity, and apparent resistivity logs are accurately reproduced with numerical simulations that honor the postinvasion radial distribution of water saturation. Examples of application include oil- and gas-bearing reservoirs that exhibit a complete capillary fluid transition between water at the bottom and hydrocarbon at irreducible water saturation at the top. We show that the estimated dynamic petrophysical properties in the water-bearing portion of the reservoir are in agreement with vertical variations of water saturation above the free water-hydrocarbon contact, thereby validating our estimation method. Additionally, it is shown that the radial distribution of water saturation inferred from apparent resistivity and nuclear logs can be used for fluid-substitution analysis of acoustic compressional and shear logs.

The Application of Committee Machine With Intelligent Systems to the Prediction of Permeability From Petrographic Image Analysis and Well Logs Data: A Case Study From the South Pars Gas Field, South Iran

Permeability is the ability of porous rock to transmit fluids. An accurate knowledge of reservoir permeability is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs, and intelligent systems to predict permeability. Petrographic image analysis was employed to measure the optical porosity, pore types, pore morphologies, mineralogy, amount of cement, and type of texture. Available conventional log measurements include bulk density, neutron porosity, and natural gamma ray. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL), and a neuro-fuzzy (NF) model. Afterwards, two types of committee machine with intelligent systems (CMIS) were used to combine the permeability values calculated from the individual intelligent systems: simple averaging and weighted averaging. In the weighted averaging, a genetic algorithm model was employed to obtain the optimal contribution of each expert. The results show that both of the CMIS performed better than NN, FL, and NF models acting alone.

Wavelet Analysis of Geophysical Well-log Data of Bombay Offshore Basin, India

Geophysical well-log (bore-hole) data facilitate understanding of the physical properties of the subsurface formations as a function of depth measured in a well. In the present study, the wavelet transformation technique was applied to the well-log data of three wells in the Bombay High oil field, India, in order to identify depths to the tops of oil and/or gas formation zones (pay zones). Continuous wavelet transformation (CWT) was performed on gamma-ray, resistivity, neutron porosity and velocity log data sets in order to determine the space-localization of the oil and/or gas formation zones. The choice of a mother wavelet is important and largely depends on the data under investigation. We have applied a variety of wavelets to the different log data sets to not only identify the depths to the tops of formation zones, but also to determine the optimum wavelet that best characterizes the pay zones. On examination of scalogram plots of each log corresponding to each wavelet for their better resolution in identifying the formation boundaries, we have found that the scalograms corresponding to the Gaus1 wavelet appeared to give the best resolution in identifying the depths of pay zones in all the well-log data sets of all three wells. To further validate the above observation, a histogram analysis of CWT coefficients is made. This showed that, of all the wavelets considered for the present study, Gaus1 wavelet is the most appropriate and optimum for determining the space-localization of pay zones in all the well-log data sets considered in the present study. The depths of pay zones estimated from scalogram plots of logs agree well with those provided by the Oil and Natural Gas Corporation Ltd., India.

ELECTROFACIES CHARACTERIZATION USING SELF-ORGANIZING MAPS

The characterization of electrofacies is essential for reservoir modeling. However, this is a process that dependends on many variables, with errors and associated noise that interfere on visual interpretation. In order to minimize uncertainties, this paper proposes the use of the artificial neural network called Auto-Maps Organizing, which is a computational algorithm inspired on the brain function that maps and groups similar information. The petrophysical data used, referes to Namorado Field on Campos Basin, of which were studied the neutron porosity, gamma ray, density and sonic profiles, to classify the field’s reservoirs lithology. From the resultsit was possible to define the reservoir geometry and to detail its features with more accuracy.

Removal of Cyclic Borehole Noise From Low- and High-Resolution LWD Images and Its Impact on Image Interpretation

Summary The use of logging-while-drilling (LWD) imaging tools in realtime decision making and post-drilling analysis has become commonplace. However, image noise and processing errors because of inherent measurement physics can propagate errors and, thus, complicate interpretation of openhole log data and images. For example, standard density images tend to amplify image noise from small borehole irregularities. Among different borehole irregularities, spiraling is known to occur more frequently with conventional rotary assemblies, steerable motor assemblies, and rotary-steerable assemblies. When the cyclic-noise amplitude from spiraling becomes large relative to the measurement of the primary interest, it grossly affects the quality of the recorded formation bulk density, photoelectric factor, and neutron porosity. This paper shows novel methods to remove cyclic noise from formation-evaluation (FE) images by applying frequency-domain filtering. Although the initial attempt of fast Fourier transforms (Zhang et al. 2010) illustrates the straightforward concept, it is seldom used because of implementation issues requiring interactive filter design and intensive operator intervention. Recently, a new method (Sugiura et al. 2011) has been designed to improve the filtering process. Additionally, the new adaptive filter designs automate the cyclic-noise-removal process from the FE images. Standalone software has been developed to process the entire image logs from one well, without human supervision. The software adaptively modifies the filter behavior as borehole-oscillation noise characteristics change with formation, drilling assemblies, hole inclination, and depth. To examine its validity qualitatively, the algorithm is then applied to various field data, not only on low-resolution density images but also on higher-resolution density images. The new algorithm has been proved by bringing considerable improvement to the image quality without any artificial interruptions. The rugosity effect is reduced significantly, and the apparent resolution of bed-boundary features is increased. Furthermore, comparative field examples illustrate the improvement in image-feature extraction. The method described here is critical, for example, for small-fracture detection and more-precise definition of formation-property contrasts indicative of bed boundaries in irregular boreholes. This new method not only is effectively used for density images but also is applicable to any other borehole images, such as resistivity and ultrasonic images.

Improved estimation of mineral and fluid volumetric concentrations from well logs in thinly bedded and invaded formations

Calculation of mineral and fluid volumetric concentrations from well logs is one of the most important outcomes of formation evaluation. Conventional estimation methods assume linear or quasi-linear relationships between volumetric concentrations of solid/fluid constituents and well logs. Experience shows, however, that the relationship between neutron porosity logs and mineral concentrations is generally nonlinear. More importantly, linear estimation methods do not explicitly account for shoulder-bed and/or invasion effects on well logs, nor do they account for differences in the volume of investigation of the measurements involved in the estimation. The latter deficiencies of linear estimation methods can cause appreciable errors in the calculation of porosity and hydrocarbon pore volume. We investigated three nonlinear inversion methods for assessment of volumetric concentrations of mineral and fluid constituents of rocks from multiple well logs. All three of these methods accounted for the general nonlinear relationship between well logs, mineral concentrations, and fluid saturations. The first method accounted for the combined effects of invasion and shoulder beds on well logs. The second method also accounted for shoulder-bed effects but was intended for cases where mud-filtrate invasion is negligible or radially deep. Finally, the third method was designed specifically for analysis of thick beds where mud-filtrate invasion is either negligible or radially deep. Numerical synthetic examples of application indicated that nonlinear inversion of multiple well logs is a reliable method to quantify complex mineral and fluid compositions in the presence of thin beds and invasion. Comparison of results against those obtained with conventional multimineral estimation methods confirmed the advantage of nonlinear inversion of multiple well logs in quantifying thinly bedded invaded formations with variable and complex lithology, such as those often encountered in carbonate formations.

Three‐dimensional stochastic estimation of porosity distribution: Benefits of using ground‐penetrating radar velocity tomograms in simulated‐annealing‐based or Bayesian sequential simulation approaches

Estimation of the three‐dimensional (3‐D) distribution of hydrologic properties and related uncertainty is a key for improved predictions of hydrologic processes in the subsurface. However it is difficult to gain high‐quality and high‐density hydrologic information from the subsurface. In this regard a promising strategy is to use high‐resolution geophysical data (that are relatively sensitive to variations of a hydrologic parameter of interest) to supplement direct hydrologic information from measurements in wells (e.g., logs, vertical profiles) and then generate stochastic simulations of the distribution of the hydrologic property conditioned on the hydrologic and geophysical data. In this study we develop and apply this strategy for a 3‐D field experiment in the heterogeneous aquifer at the Boise Hydrogeophysical Research Site and we evaluate how much benefit the geophysical data provide. We run high‐resolution 3‐D conditional simulations of porosity with both simulated‐annealing‐based and Bayesian sequential approaches using information from multiple intersecting crosshole gound‐penetrating radar (GPR) velocity tomograms and neutron porosity logs. The benefit of using GPR data is assessed by investigating their ability, when included in conditional simulation, to predict porosity log data withheld from the simulation. Results show that the use of crosshole GPR data can significantly improve the estimation of porosity spatial distribution and reduce associated uncertainty compared to using only well log measurements for the estimation. The amount of benefit depends primarily on the strength of the petrophysical relation between the GPR and porosity data, the variability of this relation throughout the investigated site, and lateral structural continuity at the site.

Non-Gaussianity of petrophysical parameters using [formula omitted] entropy and a multifractal random walk

Geological systems such as petroleum reservoirs can be investigated using Tsallis entropy and multiplicative hierarchical cascade models. The occurrence of non-Gaussianity is a sign of uncertainty and a phase transition, which could indicate the existence of a petroleum reservoir. Two important parameters that describe a system at any scale are determined: the degree of non-Gaussianity, q, for the entropy and the intermittency, λ2, which explains critical behavior in a system. Some petrophysical indicators can be used to characterize a reservoir, but there is a lack of methods for measuring scaling information. This study compares non-Gaussianity for three selected indicators at various scales: gamma radiation (GR), sonic transient time (DT) and neutron porosity (NPHI). The results show that GR has a fat-tailed probability distribution function (PDF) at all scales, which is a sign of phase transition in the system and indicates high q and λ2. This provides valuable information about GR. NPHI shows scale dependence and the PDF converges to a Gaussian distribution at large scales. This is indicative of separated and uncorrelated porosity at large scales. For the DT series, small λ2 and q at all scales are a hallmark of local DT correlations.

A simple well-logging tool using boron-lined sodium iodide scintillators and an 241AM-BE neutron source

A couple of 2-inch by 2-inch right cylinder sodium iodide scintillators and an Am-Be radioisotope neutron source have been used in a neutron porosity well-logging tool to explore the variation of hydrogen contents in a prototype formation. Both Monte Carlo N-particle transport code simulation and experimental results of the near- to far-detector responses confirm the reliable sensitivity of proposed tool to the formation porosity.

Origins of gypsum in deep carbonate reservoirs: Implications for hydrocarbon exploration and production

Gypsum can form penecontemporaneously in carbonate sediments, and it may be altered to anhydrite during burial. Some workers have suggested that gypsum dehydration is complete at burial depths of 3500 ft (1067 m); however, gypsum has been documented at depths as great as 13,100 ft (3993 m). Two examples of deeply buried gypsum illustrate contrasting origins. Gypsum in the Permian San Andres Formation in the Permian Basin of west Texas at a depth of 6000 ft (1829 m) occurs as anhydrite nodules with an outer rind of gypsum, suggesting that anhydrite was rehydrated to gypsum by interaction with interstitial brines. In contrast, gypsum in the carbonate beds of the Cretaceous Ferry Lake Anhydrite of the Mississippi Interior Salt Basin at 13,100 ft (3993 m) occurs as gypsum nodules with a rind of anhydrite, suggesting that the gypsum is primary and in the process of dehydration to anhydrite. These contrasting origins are likely a function of evaporite and carbonate stratigraphy. In the Permian Basin, a thick section of carbonate rocks is overlain by a relatively thin section of evaporites. In the Mississippi Interior Salt Basin, carbonates are interbedded with multiple evaporite beds, which inhibit circulation of interstitial brines. The burial temperature, pressure, and salinity of these formations suggest that gypsum could be preserved as primary or secondary gypsum. The presence of gypsum in reservoir rocks can affect porosity calculations, especially calculations using neutron porosity logs, which measure bound water of hydration in gypsum as porosity. Identification of gypsum at the depth of 13,100 ft (3993 m) indicates that log estimates of porosity in some deep carbonate reservoirs containing gypsum could have significant porosity and water saturation measurement errors.

Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system

Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.

Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Holder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Holder exponents of the data set 1 and the Holder exponents of the radioactive elements concentrations; data set 5: the estimated Holder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Holder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.

Time-lapse sonic logs reveal patchy CO2saturation in-situ

[1] Based on time-lapse sonic and neutron porosity logs from the Nagaoka CO2 sequestration experiment, a P-wave velocity-saturation relation at reservoir depth is retrieved. It does not coincide with either of the end-member models of uniform and patchy saturation but falls in between even if realistic error estimates for the host rock properties are considered. Assuming a random distribution of CO2 patches it is shown that the mechanism of wave-induced flow can be evoked to explain this velocity-saturation relation. Characteristic CO2 patch size estimates range from 1 to 5 mm. Such mesoscopic heterogeneity can be responsible for attenuation and dispersion in the well logging frequency band.

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Qcc(m) test in conjunction with the statistical test—the χ2(m) distribution—and the cross-wavelet transform (XWT) and wavelet coherency. The Qcc(m) test and MF-DXA are used to identify and quantify the strength of long-range cross correlations between the porosities derived based on the NPHI, DT, and RHOB logs on the one hand, and the GR log that is indicative of the presence of clay minerals in reservoir rocks, on the other hand. The Qcc(m) test describes qualitatively the presence of such cross correlations between the porosity and GR logs. Analysis by the MF-DXA method also indicates that the various porosity logs, the GR log, and the cross correlations between them are multifractal, hence confirming the long-range nature of the correlations. The results are confirmed further by the XWT, and indicate that the porosities estimated based on the NPHI logs are only weakly, if at all, affected by the natural GR radioactivity of reservoir rock and are, therefore, most reliable. The effect of length scale on the correlations and cross correlations was studied in detail. It is shown that such correlations exist at all length scales, and that they are of multifractal type that must be characterized by a spectrum of exponents.

Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modelling the Tommy Lakes Gas Field

Summary The Tommy Lakes field is located in northeastern British Columbia, Canada, and is one of the largest Middle Triassic gas pools within the western Canada sedimentary basin (WCSB). The major gas-production formation (Halfway/Doig reservoirs) at the Tommy Lakes field corresponds to shoreface sands with permeabilities ranging between 0.1 and 3 md, and porosities of 3 to12%. For the purpose of production optimization and field development, a full-field reservoir model was developed with the integration of advanced reservoir characterization, hydraulic-fracture modelling, and history-matching techniques. This study presents an integrated workflow for modelling the low-permeability Doig gas reservoir. A stochastic geostatistical reservoir model was developed on the basis of concepts emanating from an outcrop analogue analyzed with terrestrial light detection and ranging (LiDAR) technology and 60 wells that represent the fundamental rock characteristics, structure, facies? proportions, and petrophysical properties of the Doig anomalously thick sandstone bodies (ATSBs). Structural tops were interpreted from well logs and permeability/porosity relationships established from quantitative log analysis and core/log calibration. Facies were identified in cored intervals and were further grouped into four lithofacies. An artificial neural network (ANN) was used for training the logs of key wells [gamma ray (GR), neutron porosity (NPHI), and bulk density (RHOB)] and populating the facies distribution of uncored wells. Facies-based log-derived porosity, permeability, shale volume, and water saturation were assigned to gridblocks using sequential Gaussian simulation (SGS). Finally, the Monte Carlo simulation approach was used to rank the key variables affecting original gas in place (OGIP) in the uncertainty and optimization process. Flow-based techniques were used for upscaling reservoir properties into the coarse simulation grid. The full-field simulation model was calibrated with buildup data and hydraulic-fracture modelling of single wells. Production of the Doig channel from commingled wells was allocated systematically in order to achieve a good match of the gas-production history and bottomhole pressures. Sensitivity analysis of fracture half-length and its impact on ultimate gas recovery was investigated. This concluded with an integrated development strategy. It is concluded that integration of multiple domains leads to a valid full-field reservoir model, which is critical in developing an integrated strategy, predicting reservoir performance, and optimizing gas production.

Carbon dioxide storage potential for the Queenston Formation near the AES Cayuga coal-fired power plant in Tompkins County, New York

We evaluated the pore volume available for a specific potential geologic carbon dioxide (CO2) storage site in the Upper Ordovician Queenston Formation near the AES Corporation Cayuga coal-fired power plant in Tompkins County, New York. Core data collected 25 mi (40 km) from the plant reveal that the Queenston Formation is a relatively homogeneous fine- to medium-grained sandstone with hematite cement. Seismic and core data indicate that the formation was deposited in a fluvial system with mobile channels and has thickness maxima that trend north-northwest. Porosity is a major factor affecting geologic CO2 storage potential, and it is important to understand discrepancies among porosity measured from core plug, neutron porosity, density-derived porosity, and thin-section point count values. Relative to core plug–derived porosity values, the neutron porosity log is more reliable than the electron density porosity values. Thin sections reveal that hematite cement is the primary factor affecting porosity variability. Seismic, core, and well-log data suggest that in a 25-mi2 (65-km2) area surrounding this power plant, the Queenston Formation can sequester 18 million metric tons (11 million metric tons) of CO2 emission from the Cayuga power plant (8 yr of CO2 output, with a range of 3–12 yr), although many uncertainties must be better constrained to obtain a more accurate estimate. Because the Queenston Formation near the Cayuga power plant is relatively homogeneous, most of the formation at this location offers the potential for CO2 storage in its pore space.

Scaling, multifractality, and long-range correlations in well log data of large-scale porous media

Three distinct methods, namely, the spectral density, the multifractal random walk approach, and the multifractal detrended fluctuation analysis are utilized to study the properties of four distinct types of well logs from three oil and gas fields, namely, the natural gamma ray emission, neutron porosity, bulk density, and the sonic transient time logs. Such well logs have never been analyzed by the methods that we utilize in the present study. The results indicate that the well logs exhibit multifractal characteristics, and the estimated Hurst exponents by the three methods are close to each other. Using multifractal detrended fluctuation analysis and the shuffled and surrogated data, we find that the source of multifractality is due to both broad probability density functions of the data and long-range correlations in them. The correlations are persistent and are characterized by a Hurst exponent H>0.5. Despite very significant differences in the geology of the three reservoirs–ranging from shaly sands to fractured carbonate reservoirs–there is a rough universality in the log data in that, the Hurst exponents for all the logs vary in a very narrow range.