Petrophysical Parameters Of Rocks Research Articles

Determination of Hydrocarbon Production Zones using Well Logging Data in the "North_Rawa" Well of the South Sumatra Basin

Petrophysical analysis with well logging is carried out to determine the value of petrophysical parameters of rocks in the formation. Then to find out information about the subsurface structure as well as the depth zone of layers containing hydrocarbons and the types of hydrocarbons from the production field. In this research, an evaluation of reservoir formation was carried out in the North_Rawa field of the South Sumatra Basin, based on petrophysical log analysis from data from three wells in the North_Rawa field. Reservoir identification results from physical rock parameters such as porosity, shale content, water saturation and permeability obtained from formation evaluation calculations. Porosity is calculated to determine the pores, shale content is calculated to determine the volume of shale in the reservoir, water saturation is calculated to determine the water content in the formation, and permeability is calculated to determine the rock's ability to pass fluid. The results of petrophysical calculations were obtained in the hydrocarbon prospect zone in the North- rawa 1 well at a depth of 3387.5 – 3938 feet, the North- rawa 3 well at a depth of 2667.5 – 3735.5 feet, the North- rawa 4 well at a depth of 2759.5 – 3775 feet. The average porosity values for each well are 8%, 15.4%, 16%. The average shale content value for each well is 61%, 48%, 53%. Average water saturation values are 61%, 54%, 68%. Average permeability values 46.7 mD , 98.9 mD , 3.38 MD. The hydrocarbon prospect in the research well contains natural gas and oil hydrocarbons with sandstone and shale formation lithology.

Rock physics and machine learning comparison: elastic properties prediction and scale dependency

Rock physics diagnostics (RPD) established based upon the well data are used to deterministically predict elastic properties of rocks from measured petrophysical rock parameters. However, with the recent advances in statistical methods, machine learning (ML) can help to build a shortcut between raw well data and rock properties of interest. Several studies have reported the comparison of rock physics and machine learning methods for the prediction of rock properties, but the scale dependence of the ML models was never investigated. This study aims at comparing the results from rock physics and machine learning models for predicting elastic properties such as bulk density (ρb), P-wave velocity (Vp), S-wave velocity (Vs), as well as Poisson’s ratio (v) and acoustic impedance (Ip) in a well from the Gulf of Mexico (GOM) in two different scale scenarios: the well log and seismic scales. The well data under examination was split into training and testing subsets to optimize and test the developed ML models. The RPD approach was also used to validate and compare the accuracy of predicted elastic properties. Backus averaging was later applied to upscale the well data to the seismic scale to examine the scale dependence and prediction accuracy of aforementioned physics-driven and data-driven approaches. Results show that RPD and ML methods provided consistent results at both well log and seismic scales, suggesting the scale independence of both approaches. Moreover, ML models showed better estimation of rock properties due to their “apparent” match with measured data at both scales compared to the RPD approach where a significant mismatch between measured and predicted rock properties was found in the reservoir section of the well. However, by conducting further quality control of the sonic data, it was found that the measured Poisson’s ratio was extremely high in the gas-saturated interval. Hence, the prediction from ML models in this particular case cannot be trusted as ML models were trained based on poor-quality well data with non-realistic Vs and v values. Such an issue, however, could be identified and corrected using RPD as presented in this study. We demonstrate the importance of incorporating domain knowledge, i.e., rock physics, to check data quality and validate results from data-driven models.

Analysis of reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network method

SUMMARY Digital rock physics (DRP) is important for characterizing the pore characteristics and petrophysical parameters of rocks from a microscopic perspective. Among the digital rock reconstruction methods, the most widely used is the computer tomography (CT) scan method. However, the pore structure of rocks has multiscale features, and CT scan images with a single resolution cannot completely describe the pore structure characteristics of rocks. In this paper, we analysed reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network (CycleGAN) method. This method fully integrates the advantages of the large field of view of low-resolution images and the high-precision features of high-resolution images. To investigate the performance of the method, two sequences of CT scan images of a sandstone (sample A) and a carbonate rock (sample B) collected from oilfields were studied. Moreover, due to the high resolution of scanning electron microscope (SEM) images, we also constructed 3-D digital rocks with different resolutions of the Berea sandstone based on the combination of CT scan images and SEM images. Finally, the statistical properties and absolute permeabilities were calculated to evaluate the accuracies of reconstructed multisource and multiscale 3-D digital rocks. The results show that the reconstructed multiscale digital rocks based on the CycleGAN method have good accuracy in terms of statistical properties and petrophysical properties. Considering the computational cost and computational accuracy, for high- and low-resolution CT scan images and CT scan images with SEM images as training image data sources, we suggest that the resolution of the reconstructed multiscale digital cores is 4–8 times and 4–16 times higher than that of the low-resolution CT scan images, respectively. The findings of our research will be helpful in gaining insight into the petrophysical properties of heterogeneous rocks.

Joint seismic and petrophysical nonlinear inversion with Gaussian mixture-based adaptive regularization

The estimation of reservoir parameters from seismic observations is one of the main objectives in reservoir characterization. However, the forward model relating the petrophysical properties of rocks to observed seismic data is highly nonlinear, and solving the relevant inverse problem is a challenging task. We have developed a novel inversion method for jointly estimating the elastic and petrophysical parameters of rocks from prestack seismic data. We combine a full rock-physics model and the exact Zoeppritz equation as the forward model. To overcome the ill conditioning of the inverse problem and address the complex prior distribution of model parameters given lithofacies variations, we have developed a regularization term based on the prior Gaussian mixture model under a Bayesian framework. The objective function is optimized by the fast simulated annealing algorithm, during which the Gaussian mixture-based regularization terms are adaptively and iteratively adjusted by the maximum likelihood estimator, allowing the posterior distribution to be more consistent with the observed seismic data. The adaptive regularization method improves the accuracy of petrophysical parameters compared with sequential inversion and nonadaptive regularization methods, and the inversion result can be used for indicating gas-saturated areas when applied to field data.

Seismic imaging of mélanges; Pieniny Klippen Belt case study

The authors present results of the first high-resolution deep seismic reflection survey in the Pieniny Klippen Belt (PKB) in Poland. This survey sheds new light on the matter of olistostromes and the mélange character of the PKB. The sedimentary mass-transport deposits represented by olistoliths and olistostromes manifest themselves by different petrophysical parameters of rocks (velocity, density and resistivity) and seismic attributes. Seismic attributes are very effective in the interpretation of the geology of complex mélanges. The authors used selected attributes: low-pass filter, energy, energy gradient, dip-steered median filter, Prewitt filter, Laplacian edge enhancing filter and square root of the energy gradient. These attributes emphasize changes of the seismic image inside mélange zones. The distinguished olistoliths are now inside imbricated thrust structures and they are tectonically rearranged. Polygenetic mélanges in the PKB originated as a result of sedimentary and tectonic processes. The PKB in the investigated area forms several north-vergent thrust sheets belonging to the Złatne and Hulina nappes. Both nappes contain large chaotic, non-reflective olistoliths as well as the smaller mainly high-reflective olistoliths. Olistoliths are arranged parallel to the flysch layering and thrusts. The results presented confirm the postulated two olistostrome belts within the PKB structure. Thematic collection: This article is part of the Polygenetic mélanges collection available at: https://www.lyellcollection.org/cc/polygenetic-melanges

Petrophysical parameters of rocks of the Visean stage (Lokhvytsky zone of the Dnieper-Donets Depression)

The results of complex investigations of petrophysical properties of the Visean rocks from wells in Lokhvytsky zone of the DDD (sandstones and siltstones from depths 5060―5600 m and limestones from depths 5960―6200 m) are presented. The open porosity coefficient, specific and relative electrical resistivity, velocity of P-waves and S-waves, three-dimensional magnetic susceptibility of rocks are determined. The studies of electric and acoustic properties were carried out in atmospheric and simulated stratal conditions on dry and water saturated stratal model rocks. Relative decrease of the porosity coefficient with changing from atmospheric conditions to reservoir conditions is from 5,5 to 25 % (mean 11,5 %). The average value of specific electrical resistivity of sandstones is 1.2 times more than of siltstones, and 12 times more than of limestones. It has been found that due to the closure of micro-cracks and the deformation of the pore space, the electrical resistance of rocks tends to increase with pressure. There is no significant distinction in velocity parameters between dry sandstones and siltstones, however, limestones are characterized by higher velocity (approximately 30 %) of transversal waves. Saturated rocks have approximately the same average values of S -wave velocity. The average value of velocity of S -waves in sandstones is 17 % higher than in siltstones. It was established that three-dimensional magnetic susceptibility varies in wide limits. Sandstones are characterized by the highest value, this parameter for limestones is 58 times lower than for sandstones. By the mean values of the physical parameters of sandstones and siltstones, there is clearly a direct relationship between the magnetic susceptibility, density and electrical resistivity, and the reverse one with porosity. The results obtained can be used to determine the perspectivity of rocks of different composition, to modeling their properties for different depths and to quantify the structure of their pore spaces. Received dependencies between the various parameters permit to clarify the interpretation of wells geophysical researching data of the studied area.

A MODEL OF TECTONIC STRUCTURE AND EVOLUTION OF THE ABU GHARADIG BASIN BASED ON NEW HYDROCARBON EXPLORATION RESULTS, WESTERN DESERT PROVINCE, EGYPT

Structural traps in the deep reservoirs of the Lower Cretaceous Kharita and Alam el Bueib as well as Middle Jurassic Khatatba formations are on the exploration agenda for the Abu Gharadig basin in the Western Desert petroleum province of Egypt. A new seismic and litho-stratigraphic interpretation made by SE Naukanaftogaz substantiates further development of hydrocarbon potential of the Alam El Shawish concession block operated by Petrosannan Co. (JV of EGPC and NJSC Naftogaz of Ukraine Overseas in A.R.E.). Recently acquired 3D seismic dataset, parametric analysis of seismic wave field to predict spatial development of improved reservoir properties combining with drilling, well-to-well correlation of strata and petrophysical rock parameters coupling with testing results have enabled to propose a new stratigraphic and tectonic model of the area studied that offers an upside exploration prospectivity for several deep structural traps related to pop-up and drape anticline structures derived from wrench faulting and local structural inversion of the Abu Gharadig basin.

Petrophysical parameters of rocks from the areas of eastern sector of the Dnieper-Donets depression promising for shale gas

Consideration is given to feature the method and results of petrophysical investigations by studying complex-built terrigenous reservoirs. Results of evaluation petrophysical parameters and their link with capacitive properties of sandstones, siltstone and mudstones from prospective for shale gas wells intervals of eastern sector of DDB are shown. Correlation dependences of petrophysical parameters from capacitive properties of investigated rocks have been plotted.

The bedrock electrical conductivity map of the UK

Airborne electromagnetic (AEM) surveys, when regionally extensive, may sample a wide-range of geological formations. The majority of AEM surveys can provide estimates of apparent (half-space) conductivity and such derived data provide a mapping capability. Depth discrimination of the geophysical mapping information is controlled by the bandwidth of each particular system. The objective of this study is to assess the geological information contained in accumulated frequency-domain AEM survey data from the UK where existing geological mapping can be considered well-established. The methodology adopted involves a simple GIS-based, spatial join of AEM and geological databases. A lithology-based classification of bedrock is used to provide an inherent association with the petrophysical rock parameters controlling bulk conductivity. At a scale of 1:625k, the UK digital bedrock geological lexicon comprises just 86 lithological classifications compared with 244 standard lithostratigraphic assignments. The lowest common AEM survey frequency of 3 kHz is found to provide an 87% coverage (by area) of the UK formations. The conductivities of the unsampled classes have been assigned on the basis of inherent lithological associations between formations. The statistical analysis conducted uses over 8 M conductivity estimates and provides a new UK national scale digital map of near-surface bedrock conductivity. The new baseline map, formed from central moments of the statistical distributions, allows assessments/interpretations of data exhibiting departures from the norm. The digital conductivity map developed here is believed to be the first such UK geophysical map compilation for over 75 years. The methodology described can also be applied to many existing AEM data sets.

Determinação de propriedades petrofísicas de rochas via simulação: um caminho interdisciplinar

No presente trabalho, tratamos de um problema vindo das ciências aplicadas, a potencialização da extração petrolífera, com o objetivo de destacar seu caráter interdisciplinar. Acompanhamos as principais etapas da determinação de parâmetros petrofísicos de rochas via simulação de fenômenos físicos. Intentamos a exposição dos conceitos pertinentes e nos referimos à literatura para detalhes técnicos específicos. A importância dos ensaios experimentais no desenvolvimento de algoritmos simulatórios, assim como a relevância da colaboração de profissionais de diferentes áreas para uma pesquisa científica abrangente são abordados em uma breve discussão no final do texto. A intenção é mostrar a amplitude das aplicações da física, discutindo as aproximações e limitações da descrição do problema em questão.

Pattern recognition of geophysical data

A new rock classification method for ground penetrating radar (GPR) data is presented for cases where no additional geological information is available from boreholes. There are non-linear relationships between petrophysical properties of rocks and electromagnetic waves which can be handled using two methods derived from statistical learning theory on pattern recognition. An investigation was carried out looking at proving the feasibility of the method in principle for use on synthetic models as well as measurement data. The different learning methods were also compared. The method is based on multivariate statistical learning algorithms for the discrimination of layer boundaries between different rocks. The discrimination developed works with artificial neural networks (ANN) and support vector machines (SVM). The processing procedure starts with geological models with varying petrophysical rock parameters, which are to be sought in the measurement data. The models are used to generate synthetic radargrams from which rock properties can be derived using wave attributes. The calculated values of the wave attributes are stored in a multivariate data pool. This data pool is used to train the ANN and the SVM. The same wave attributes are derived from the GPR data and also saved in a data pool. This generates two data sets for pattern recognition with which to directly classify rock layers. Wave attributes can therefore be used to derive the non-linear correlative relationships between rock properties and GPR data by the weighted matrices of ANN and SVM. The presented method can be used to match reflections in the GRR data directly with the layer boundaries of rock formations. The classification of a boundary horizon between rock salt and anhydrite is demonstrated on synthetic GPR traces and measurement data from a rock salt mine. The advantage of this method is that rock classification is not a priori dependent on borehole data.

A rule based fuzzy model for the prediction of petrophysical rock parameters

A rule based fuzzy model for the prediction of petrophysical rock parameters

A rule based fuzzy model for the prediction of petrophysical rock parameters

A new approach for the prediction of petrophysical rock parameters based on a rule-based fuzzy model is presented. The rule-based fuzzy model corresponds to the Takagi–Sugeno–Kang method of fuzzy reasoning proposed by Sugeno and his co-authors. This fuzzy model is defined by a set of fuzzy implications with linear consequent parts, each of which establishes a local linear input–output relationship between the variables of the model. In this approach, a fuzzy clustering algorithm is combined with the least-square approximation method to identify the structure and parameters of the fuzzy model from sets of numerical data. To verify the effectiveness of the proposed fuzzy modeling method, two examples are developed using core and electrical log data from three oil wells in Ceuta Field, Lake Maracaibo Basin. The numerical results of the fuzzy modelling method are compared with the results of a conventional linear regression model. It is shown that the fuzzy modeling approach is not only more accurate than the conventional regression approach but also provides some qualitative information about the underlying complexities of the porous system.

Predictive Model for Fracture Distribution and Intensity in Sandstone and Carbonate Reservoirs, Wertz and Lost Soldier Fields, Wyoming: ABSTRACT

The Tensleep Sandstone and Madison carbonate reservoirs at Wertz and Lost Soldier fields, southern Wyoming, were the subject of a fracture distribution study as part of a tertiary development study. A fracture classification scheme for the reservoirs was developed, and foot-by-foot fracture description of 15 cores throughout the two fields were generated. The core fracture classifications and descriptions were digitized for computer use. A computer-generated second-derivative map of the Tensleep structure accurately reflected fracture distribution as described from cores in both the sandstone and carbonate reservoirs. Computer-generated crossplots of number of fractures per foot versus a variety of petrophysical rock parameters identified structural position and lithology as the key factors governing fracture distribution and intensity in the Tensleep and Madison reservoirs.