Estimation Of Petrophysical Properties Research Articles

Petrophysical Properties Estimation by Using Well Log and Core Data Interpretation for Tertiary Reservoir in Ajeel Oil Field

Understanding the petrophysical properties of reservoirs is paramount for assessing their characteristics and estimating the potential of oil-bearing formations. This study focuses on the tertiary reservoir in the Ajeel oil field, renowned for its heterogeneous carbonate nature and diverse production formations, including Jeribe, Dhiban, and Euphrates. The formation of the tertiary reservoir is evaluated through a comprehensive analysis of petrophysical properties utilizing well log data from well AJ-25 and core analysis data (routine and special analysis). The study reveals significant insights into the petrophysical properties of the formations. The effective porosity values for the Jeribe, Dhiban, and Euphrates formations are determined as 15.41%, 4.65%, and 9.85%, respectively, with lithology predominantly consisting of dolomite rock interspersed with limestone slices and anhydrite nodules. A high Secondary Porosity Index (SPI) indicates fractures. Archie's Parameters, obtained from a Pickett Plot, yield cement coefficient (m=1.8), saturation exponent (n=1.9), and tortuosity factor (a=1.19). Water saturation levels for the Jeribe, Dhiban, and Euphrates formations are 33%, 86%, and 81%, respectively, with porosity and water saturation cutoff values determined as 10% and 60%, respectively. Net to Gross of Jeribe, Dhiban, and Euphrates formation are 0.706, 0.029, and 0.011, respectively. Jeribe Formation is identified as an oil-bearing zone. In contrast, the Dhiban and Euphrates formations are identified as water-bearing zones. To enhance the accuracy of petrophysical assessments, modern well logging techniques, such as the Formation Micro Scanner log (FMI log), are recommended to investigate fractures and their impact on field performance, as traditional logs may not adequately capture these features. Additionally, our comparative analysis highlights superior petrophysical properties of the Euphrates Formation compared to the Jeribe Formation. Therefore, prioritizing drilling and completion activities in the Euphrates Reservoir within the field, contingent upon its positioning relative to the oil-water contact level, is suggested for optimizing reservoir performance.

Bayesian neural network and Bayesian physics-informed neural network via variational inference for seismic petrophysical inversion

Bayesian neural network via variational inference (BNN-VI) aims to estimate probability distribution of the training parameters of the neural network, which are difficult to compute with traditional methods, by proposing a family of densities and finding the candidates that are close to the target. Variational inference is a Bayesian method that provides a faster tool than Markov chain Monte Carlo sampling algorithms. However, to provide an accurate estimation of the most likely models, BNN-VI requires sufficient training data, which is not always available in geophysical inverse problems. We propose a method that combines a Bayesian approach with a physics-informed neural networks algorithms via variational inference, namely Bayesian physics-informed neural networks via variational inference (BPINN-VI) and apply it to a geophysical inverse problem for the estimation of petrophysical properties from seismic data. The method is based on a Bayesian network approach in which the objective function is defined by the Kullback–Leibler (KL) divergence. In addition, the physical relations between data (seismic measurements) and model variables (petrophysical properties) are embedded into neural networks through a physics-dependent loss term. We tested the proposed method on a pre-stack seismic dataset from the North Sea with limited direct measurements of the target petrophysical properties from well logs. The outcome is the most likely model of petrophysical properties. Compared to BNN-VI, the BPINN-VI results show higher correlation and R2 coefficients, and lower mean square error as well as lower uncertainty and higher lateral continuity.

Enhancing digital rock analysis through generative artificial intelligence: Diffusion models

Within the realm of computer vision, the landscape has been significantly reshaped by the abundance of extensive and diverse datasets, leading to remarkable breakthroughs in image processing. These advancements have reverberated across a wide spectrum of applications, catalyzing transformative outcomes. However, in stark contrast, the field of digital rock analysis finds itself grappling with a conspicuous dearth of data, a challenge that casts a formidable shadow over the effective deployment of computer vision techniques for rock image analysis. In response to this pressing issue, this paper presents a pioneering methodology designed to surmount the hurdles posed by data limitation in the realm of digital rock analysis. At the core of this innovative approach lies the fusion of artificially generated digital rock images, created using a state-of-the-art diffusion model, with their authentic counterparts. This fusion is guided by the overarching objective of augmenting the efficacy of various digital rock analysis applications. This integration endeavors to bridge the gap between the limited available data and the substantial demands of the digital rock analysis domain. The practical significance and potential of this integrated approach are vividly demonstrated through a series of concrete implementations. These include, but are by no means limited to, enhancing image quality to facilitate clearer visualization of intricate rock structures and refining the estimation of petrophysical properties with increased accuracy.

A Pore-Scale Model for Electrokinetic In situ Recovery of Copper: The Influence of Mineral Occurrence, Zeta Potential, and Electric Potential

Electrokinetic in-situ recovery is an alternative to conventional mining, relying on the application of an electric potential to enhance the subsurface flow of ions. Understanding the pore-scale flow and ion transport under electric potential is essential for petrophysical properties estimation and flow behavior characterization. The governing physics of electrokinetic transport is electromigration and electroosmotic flow, which depend on the electric potential gradient, mineral occurrence, domain morphology (tortuosity and porosity, grain size and distribution, etc.), and electrolyte properties (local pH distribution and lixiviant type and concentration, etc.). Herein, mineral occurrence and its associated zeta potential are investigated for EK transport. The new Ek model which is designed to solve the EK flow in complex porous media in a highly parallelizable manner includes three coupled equations: (1) Poisson equation, (2) Nernst–Planck equation, and (3) Navier–Stokes equation. These equations were solved using the lattice Boltzmann method within X-ray computed microtomography images. The proposed model is validated against COMSOL multiphysics in a two-dimensional microchannel in terms of fluid flow behavior when the electrical double layer is both resolvable and unresolvable. A more complex chalcopyrite-silica system is then obtained by micro-CT scanning to evaluate the model performance. The effects of mineral occurrence, zeta potential, and electric potential on the three-dimensional chalcopyrite-silica system were evaluated. Although the positive zeta potential of chalcopyrite can induce a flow of ferric ion counter to the direction of electromigration, the net effect is dependent on the occurrence of chalcopyrite. However, the ion flux induced by electromigration was the dominant transport mechanism, whereas advection induced by electroosmosis made a lower contribution. Overall, a pore-scale EK model is proposed for direct simulation on pore-scale images. The proposed model can be coupled with other geochemical models for full physicochemical transport simulations. Meanwhile, electrokinetic transport shows promise as a human-controllable technique because the electromigration of ions and the applied electric potential can be easily controlled externally.Graphical abstract

Semi-Automated Rock Classification for Permeability Estimation Using High-Resolution Computed Tomography Scan Images, Core Photos, and Well Logs

Summary Reliable estimation of petrophysical properties can be challenging, especially in geological formations with rapid variation in the spatial distribution of rock components. The spatial distribution of rock components, also known as rock fabric, is often not captured by conventional well logs that are typically used for the estimation of petrophysical properties. The aforementioned challenge is rooted in the limited vertical resolution of conventional well logging tools. Alternatively, specialized tools and techniques, such as nuclear magnetic resonance (NMR) and computed tomography (CT) scan images, can provide information on the variation in the rock fabric of geological formations. The objectives of this paper are (a) to use 2D CT scan images and core photos, conventional well logs, NMR logs, and core-measured properties for semi-automated rock classification, (b) to develop class-based rock physics models for enhanced petrophysical properties estimation, and (c) to provide a method to expedite the detection of quantitative image-based rock classes in cored wells. First, we conducted conventional formation evaluation (CFE) for the initial assessment of petrophysical properties. Then, we implemented three different rock classification techniques for class-based estimation of petrophysical properties. The first of the rock classification techniques uses routine core analysis (RCA) data to define hydraulic units. The second rock classification technique uses NMR data to characterize the changes in pore-size distribution of the evaluated formation. The last rock classification technique integrates quantitative image-based features from CT scan images and core photos with NMR data. Finally, the obtained rock classes from the abovementioned rock classification techniques are used to derive class-based permeability models. We applied the proposed workflow to a pilot well drilled in a saline water aquifer formation that will be used for CO2 injection and storage in the Northern Lights carbon capture and storage (CCS) project. The extracted image-based rock fabric features were in agreement with the visual aspect of the evaluated depth intervals. The detected rock classes captured the fluid-flow behavior using a permeability-based cost function in two of the implemented rock classification techniques, the variation in petrophysical and compositional properties through well logs, and quantitative rock fabric of the evaluated depth interval through the core image data. Finally, the use of class-based rock physics models improved permeability estimates, decreasing the mean relative error by up to 37% compared with formation-based permeability estimates from a conventional method (formation-based porosity-permeability correlations). One of the key contributions of the proposed workflow is that it integrates conventional well logs, core-measured properties, NMR logs, and high-resolution image data. As a result, the obtained integrated rock classes capture key petrophysical and geological parameters of the evaluated depth intervals that are typically not included in rock classification efforts. The obtained integrated rock classes can potentially improve the development of accurate geological models, which are used in simulation efforts as a screening tool for the selection of geological formations for CO2 storage as well as for storage capacity, selection of CO2 injection intervals, and containment forecasting.

Effect of spatial variability of downhole geophysical logs on machine learning exercises

Machine Learning (ML) based estimation of petrophysical properties from downhole geophysical logs has attracted a significant attention from across the geoscience community in recent years. The main focus of the studies in the area is to identify the most accurate ML algorithm for petrophysical property prediction using logging data often from a very limited number of boreholes (e.g. only one or two). Despite having vertical variability in one borehole, the horizontal variability cannot be captured using limited number of boreholes thus the effect of spatial variability on ML based models' performance needs a specific attention. We have therefore developed an innovative workflow that assesses the impact of spatial variation in training geophysical logs data on the accuracy of ML models in predicting formation properties e.g. compressional wave velocity. To build this systematic workflow, a large dataset containing geophysical logs from 50 boreholes in a coal mine in Bowen Basin, QLD, Australia was selected for the analysis. Boreholes mainly intersected geological formations from Rangal coal measure rocks. The data included density, compressional wave velocity (VP), gamma ray, resistivity and depth. The workflow consists of i) a clustering analysis using the K-mean algorithm to determine if the data could be partitioned into smaller, more homogeneous clusters based on density, gamma ray and measured depth, ii) putting aside five boreholes for validation (these boreholes are not seen by the models, named test boreholes) and iii) selectin training datasets randomly from the remaining boreholes grouping them into 1, 5, 10, 20, 30 and 40 boreholes. The data selection from each group was repeated randomly five times to capture the potential randomness; and iv) the Least Squares Support Vector Regression model of each group was trained with 10, 20, 30, …, 90 and 100% of the data of each group forming 300 predictive models. The accuracy of developed models to predict VP from other logs was evaluated by calculating R2 value comparing the estimated and the actual VP values for five test boreholes. The results from the proposed workflow interestingly suggest that the model should prioritize incorporating data from various boreholes rather than collecting more data points from a limited number of boreholes. This observation is mainly linked to high lateral variability in the study area. Consequently, utilizing a smaller portion of data from a larger number of boreholes yields a simpler yet more accurate predictive model, which also reduces the computational cost.

Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric

Supervised learning algorithms can be employed for the automation of time-intensive tasks, such as image-based rock classification. However, labeled data are not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper are (a) to train two supervised learning models for image-based rock classification employing image-based features from computerized tomography (CT) scan images and core photos, (b) to conduct image-based rock classification using the trained model, (c) to compare the results obtained using supervised learning models against an unsupervised learning-based workflow for rock classification, and (d) to derive class-based petrophysical models for improved estimation of petrophysical properties First, we removed non-formation visual elements from the core image data, such as induced fractures, the core barrel, and the seal peel tag on core photos. Then, we computed image-based features such as grayscale, color, and textural features from core image data and conducted feature selection. Then, we employed the extracted features for model training. Finally, we used the trained model to conduct rock classification and compared the obtained rock classes against the results obtained from an unsupervised image-based rock classification workflow. This workflow uses image-based rock fabric features coupled with a physics-based cost function for the optimization of rock classes. We applied the workflow to one well intersecting three formations with rapid spatial variation in rock fabric. We used 60% of the data to train a random forest and a support vector machines classifier using a 5-fold cross-validation approach. The remaining 40% of the data was used to test the accuracy of the supervised models. We established a base case of unsupervised learning rock classification and four different cases of supervised learning rock classification. The highest accuracy obtained for supervised rock classification was 97.4%. The accuracy obtained in the unsupervised learning rock classification approach was 82.7% when compared against expert-derived lithofacies. Class-based permeability estimates decreased the mean relative error by 34% and 35% when compared with formation-based permeability estimates, for the supervised and unsupervised approaches, respectively. The highest accuracies for the supervised and unsupervised models were obtained when integrating features from CT-scan images and core photos, highlighting the importance of feature selection for machine-learning workflows. A comparison of the two approaches for rock classification showed higher accuracy obtained from the supervised learning approach. However, the unsupervised method provided reasonable accuracy as well as a more general and faster approach for rock classification and enhanced formation evaluation.

Estimation of Petrophysical Properties for Zubair Reservoir in Abu-Amood Oil Field

The petrophysical analysis is significant to determine the parameters controlling the production wells and the reservoir quality. In this study, Using Interactive petrophysics software to analyze the petrophysical parameters of five wells penetrated the Zubair reservoir in the Abu-Amood field to evaluate a reservoir and search for hydrocarbon zones. The available logs data such as density, sonic, gamma ray, SP, neutron, and resistivity logs for wells AAm-1, AAm-2, AAm-3, AAm-4, and AAm-5 were used to determine the reservoir properties in Zubair reservoir. The density-neutron and neutron-sonic cross plots, which appear as lines with porosity scale ticks, are used to distinguish between the three main lithologies of sandstone, limestone, and dolomite. The corrected gamma ray log was used in all wells to determine the shale volume. Neutron-sonic log was used to calculate porosity at the reservoir unit while sonic log was employed to estimate the porosity at poor hole conditions and (non- reservoir units). Furthermore, the Indonesia model was used to calculate water saturation in Zubair reservoir and compared with the Archie model. Finally, Flow zone indicator method was used for permeability evaluation. The results show that the Zubair reservoir is primarily consists of sandstone, shale with compacted limestone, which was improved by the cuttings description report. The core porosity was validated in AMM-1, AMM-2, AMM-3and AMM-5 wells. Indonesia equation provides an optimum estimation in shaly sand zones since the Archie model takes the matrix conductive and the fluid conductivity into account. Four hydraulic flow units produced from reservoir quality index against normalized porosity index cross plot and obtained Four equations from porosity- permeability relationship of core data. The general interpretation presented that the most of hydrocarbons are located in depth (3332.8 m to 3415.8 m) which represents the best layer in Zubair reservoir, the properties of this layer in well AMM-1better than in well AMM-2. Finally, the layer from 3574.8 m to3638.3 m supposed to be water layer rather than hydrocarbon layer.

Prediction of Petrophysical Properties Through Comparative Post-Stack Inversion Techniques Using Advance Neural Networking

The sophisticated seismic inversion methodology develops the relationship between the interpreted seismic data and the elastic properties to discerning the desired facies in the reservoir characterization. The study is focused on the C-interval sand of the Lower Goru Formation by utilizing Post-stack Time Migration (PSTM) 3D seismic and borehole logs of six wells. The challenges in the facies discrimination arise due to various complexities, i.e., lithological variability and acquisition sensitivity of recording tools. Various quantitative interpretation techniques such as Band limited inversion, Model-Based inversion, and stochastic inversion are developed to handle the limitations effectively and precisely access the producing facies. A comparative analysis is performed for the heterogeneous sands by employing these QI techniques and evaluating their effectiveness. The inverted seismic attributes are further utilized in the petrophysical properties estimation through Probabilistic Neural Networking (PNN) algorithm to distinguish litho-facies by developing a petro-elastic relationship. Probabilistic Neural Networking works best for the heterogeneous reservoir’s petrophysical properties estimation, especially sand reservoirs with shales intercalation. The concluding results declare the efficiency of all applied techniques for potential sand zones, especially the stochastic inversion. The petrophysical properties volume of clay and effective porosity, estimated from the stochastic inversion attribute, matched with the results of the blind well, resolute precisely the reservoir potential, and delineated the depositional environment of the three segregated sand bodies.

Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples

The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics, and wave propagation models. The main goal of seismic reservoir characterization is to predict the rock and fluid properties given a set of seismic measurements by combining geophysical models and mathematical methods. This modeling challenge is generally formulated as an inverse problem. The most common geophysical inverse problem is the seismic (or elastic) inversion, i.e., the estimation of elastic properties, such as seismic velocities or impedances, from seismic amplitudes and traveltimes. The estimation of petrophysical properties, such as porosity, lithology, and fluid saturations, also can be formulated as an inverse problem and is generally referred to as rock-physics (or petrophysical) inversion. Several deterministic and probabilistic methods can be applied to solve seismic inversion problems. Deterministic algorithms predict a single solution, which is a “best” estimate or the most likely value of the model variables of interest. In probabilistic algorithms, on the other hand, the solution is the probability distribution of the model variables of interest, which can be expressed as a conditional probability density function or a set of model realizations conditioned on the data. The probabilistic approach provides a quantification of the uncertainty of the solution in addition to the most likely model. Our goal is to define the terminology, present an overview of probabilistic seismic and rock-physics inversion methods for the estimation of petrophysical properties, demonstrate the fundamental concepts with illustrative examples, and discuss the recent research developments.

CNN-Based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input–Output Parameters and Network Architecture

Accurate estimation of petrophysical properties (e.g., porosity, clay volume) of subsurface rock from seismic data/elastic properties is significant to reservoir characterization. Conventional model-driven inversion strategies for estimating petrophysical parameters confront with the deficiency of prior knowledge. In contrast, machine learning-based approaches are adapted to account for reservoir parameter estimation through developing nonlinear mapping and quantifying uncertainty. However, most of the current researches mainly concentrates on the single parameter prediction with different neural network architectures, which, in turn, conflicts with the truth of coupling multiple reservoir properties. To quantify the sensitivity of input-output parameters and the effects of network architecture on the accuracy of petrophysical parameter inversion, we propose a CNN-based network strategy to estimate multiple reservoir parameters simultaneously. The results from both synthetic labeled data and field data and uncertainty analysis strongly demonstrate that SopenCNN, abbreviated from multiple input and single output openCNN, exhibits the highest prediction accuracy, while the cycleCNN with multiple input and multiple output, referred to as McycleCNN, is superior to the MopenCNN, which means that an openCNN contains multiple input and multiple output. It means that, for similar network architecture, the number of input and output parameters makes a significant impact on prediction accuracy. Moreover, for similar multiple inputs and outputs, the fine-tuning McycleCNN, parallelly updated in each intermediate closed-loop step, behaves much better accordingly. The application of the three workflows with varying architecture on field tight sandstone reservoirs demonstrates that network based inversion strategy could establish a mapping function to characterize spatially varying reservoir parameters.

Petrophysical Properties Estimation of Euphrates Reservoir in Qayyarah Oil Field Using Core and Well Log Data

The Early Miocene Euphrates Formation is characterized by its oil importance in the Qayyarah oil field and its neighboring fields. This study relied on the core and log data analyses of two wells in the Qayyarah oil field. According to the cross-plot’s information, the Euphrates Formation is mainly composed of dolomite with varying proportions of limestone and shale. Various measurements to calculate the porosity, permeability, and water saturation on the core samples were made at different depths in the two studied wells Qy-54 and Qy-55. A relationship between water saturation and capillary pressure has been plotted for some core samples to predict sites of normal compaction in the formation. The line regression for this relationship was considered as a function of the ratio of large voids to the total volume of voids in the sample. The coefficient of determination parameter was used in estimating the amount of homogeneity in the sizes of the voids, as it was observed to increase significantly at the sites of shale. After dividing the formation into several zones, the well log data were analyzed to predict the locations of oil presence in both wells. The significance of the negative secondary porosity in detecting the hydrocarbon sites in the Euphrates Formation was deduced by its correspondence with the large increase in the true resistivity values in both wells. More than 90% of the formation parts represent reservoir rocks in both wells, but only about 75% of them are oil reservoirs in the well Qy-54 and nearly 50% of them are oil reservoirs in the well Qy-55.

An integrated approach for reservoir characterisation in deep-water Krishna-Godavari basin, India: a case study

Abstract The Miocene reservoirs in prolific Krishna-Godavari basin are mostly fluvial deposits and laminated or blocky in nature. The type of reservoir quality depends on associated geological environments. Due to several lateral variations in reservoir properties, a similar kind of workflow for reservoir characterisation does not work. Customised workflow needs to be applied in this area for estimation of petrophysical properties or rock physical analysis for reservoir quality prediction. As the major input of rock physical analysis is petrophysical properties, it is crucial to estimate these properties accurately. Meanwhile, it is also important to check the seismic sensitivity to change in fluid saturation in the reservoir characterisation process. The analysis assures the presence of reservoir and hydrocarbon contact in seismic sensitivity, which is essential for removing risk. Integrating the geological model with rock physical analysis for reservoir characterisation at the drilled well, the reservoir quality at undrilled prospects is predicted. In this study, the comprehensive study for reservoir characterisation of Miocene reservoirs consists of three different steps: calculation of petrophysical properties for mixed of thick and laminated sequence, rock physical analysis for identification of hydrocarbon reservoir and corresponding seismic sensitivity for change in saturation and finally the rock physics template for prediction of reservoir quality away from the drilled well. Results from the study have added significant value in de-risking the number of undrilled prospects in this area.

Understanding reservoir heterogeneity using variography and data analysis: an example from coastal swamp deposits, Niger Delta Basin (Nigeria)

Abstract For efficient reservoir management and long-term field development strategies, most geologists and asset managers pay special attention to reservoir chance of success. To minimise this uncertainty, a good understanding of reservoir presence and adequacy is required for better ranking of infill opportunities and optimal well placement. This can be quite challenging due to insufficient data and complexities that are typically associated with areas with compounded tectonostratigraphic framework. For the present paper, data analysis and variography were used firstly to examine possible geological factors that determine directions in which reservoirs show minimum heterogeneity for both discrete and continuous properties; secondly, to determine the maximum range and degree of variability of key reservoir petro-physical properties from the variogram, and thirdly, to highlight possible geological controls on reservoir distribution trends as well as areas with optimal reservoir quality. Discrete properties evaluated were lithology and genetic units, while continuous properties examined were porosity and net-to-gross (NtG). From the variogram analysis, the sandy lithology shows minimum heterogeneity in east-west (E–W) and north-south (N–S) directions, for Upper Shoreface Sands (USF) and Fluvial/Tidal Channel Sands (FCX/TCS), respectively. Porosity and NtG both show the least heterogeneity in the E–W axis for reservoirs belonging to both Upper Shoreface and Fluvial Channel environments with porosity showing a slightly higher range than NtG. The vertical ranges for both continuous properties did not show a clear trend. The Sequential Indicator Simulation (SIS) and Object modelling algorithm were used for modelling the discrete properties, while Sequential Gaussian Simulation (SGS) was used for modelling of the continuous properties. Results from this exercise show that depositional environment, sediment provenance, topographical slope, sub-regional structural trends, shoreline orientation and longshore currents, could have significant impacts on reservoir spatial distribution and property trends. This understanding could be applied in reservoir prediction and for generating stochastic estimates of petrophysical properties for nearby exploration assets of similar depositional environments.

Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

Caveats of using fractal analysis for clay rich pore systems

The conventional techniques cannot accurately predict the multiphase flow behaviour in clay rich low permeability rocks. This is particularly important in applications such as hydraulic fracturing where fluid uptake into low permeability rocks is of interest. This inability has given rise to the use of fractal-based estimation of petrophysical properties of such rocks. The concept of fractal theory revolves around the fractal dimension (Df) representing the entire pore structure which is often estimated using the mercury-intrusion capillary pressure (MICP) data among other techniques. However, rocks containing high clay content often exhibit higher capillary pressures than those based on MICP. This is due to interaction of clay minerals with external fluids such as hydraulic fracturing fluid and subsequent transient change in pore structure. This also in turn causes the Df value to vary thus leading to errors and uncertainties in fractal-estimated petrophysical properties of clay rich rocks. This transient aspect of clay rich rocks’ pore network is not yet fully understood nor systematically investigated or incorporated in current fractal methods. We have therefore conducted a comprehensive study to narrate a cautionary tale on the use of MICP-based fractal methods in clay rich tight rocks analyses. We first highlighted the uncertainty in the MICP-based Df value by comparing experimental and simulated oil recoveries from three clay-rich low permeability sandstone samples. We then developed, for the first time, a new set of physical equations to describe the transient pore network based on MICP-based fractal theory in order to partially overcome these uncertainties. We next compared the new theoretical model with experimental oil-water capillary pressure data. Through such rigorous analyses, it was concluded that MICP-based fractal theory relying on an unchanging pore structure is not accurate in its current form. Although our new approach can address the issue with the transient nature of clay rich pore network, there is yet immense space for improvement to overcome the limitations associated with fractal based petrophysical property estimation.

Enhanced Formation Evaluation in the Permian Basin Using a Novel Field-Scale Workflow Including Wells with Missing Data

Summary Reliable formation evaluation in organic-rich mudrocks requires integrated interpretation of well logs and core measurements. More than 80% of the Permian Basin wells have incomplete data sets, lacking photoelectric factor (PEF) or other logs required for reliable formation evaluation in the presence of complex mineralogy. Hence, we develop a novel workflow to reliably estimate rock properties in wells with incomplete data to enhance reservoir characterization and completion decisions. We propose to use integrated rock classification for enhanced physics-based assessment of rock properties in wells with missing data; combine field-scale geostatistical and machine learning methods to reliably reconstruct missing PEF logs with a confidence interval through a rock-type-based approach, which is a unique contribution of this work; and quantify the uncertainty in estimates of petrophysical properties. We performed a preliminary field-scale formation evaluation on wells with triple-combo logs (more than 70 wells). Next, we performed an initial rock typing and reconstructed the missing PEF logs by combining supervised neural networks with geostatistical analysis on a rock-type basis. We then used an unsupervised neural network method to improve the rock classification based on the updated estimates of petrophysical and compositional properties after PEF reconstruction. The combined rock classification and PEF reconstruction was performed iteratively to improve the multimineral analysis results in all wells with missing data. We successfully applied the new workflow to 20 wells in blind tests. The reconstructed well logs agreed with the actual measurements with relative errors of less than 10%. The new workflow extends the boundaries of reliable formation evaluation, enabling accurate reservoir characterization and completion decisions by enhancing evaluation of wells with missing data. The proposed method can also be applied to wells with other types of missing data.

An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

Abstract The main reservoirs in the Linguado Field are grainstone and packstone carbonates composed of oolites, oncolites, pelloids, and rare bioclasts, of the Quissama Formation (Albian-age) and coquinas of the Coqueiros Formation (Aptian-age). Within Quissama Formation, facies have high porosity variations and their 3D characterization is of utter importance to allow for a better location of the wells. The objective of this study was a volumetric characterization of Quissama Formation by means of an adapted classical workflow composed of well-seismic tie, seismic preconditioning, seismic interpretation, seismic inversion, petrophysical properties estimation, and porosity geostatistical modeling. Seismic inversion results allowed the interpretation of a horizon to separate the carbonate platform in an upper zone, with low acoustic impedance, from a lower zone, with high acoustic impedance. Finally, behavioral knowledge of acoustic impedance and the porosity distribution within the Albian carbonate platform helped to better characterize the distribution of carbonaceous facies allowing the confirmation that commercial wells were drilled in low acoustic impedance and high porosity regions. It was also possible to confirm that a well, declared as noncommercial, was drilled in a poor reservoir region with high acoustic impedance and low porosity and identified other locations that were not yet drilled but could represent potential targets.

Mechanistic model of multi-frequency complex conductivity of porous media containing water-wet nonconductive and conductive particles at various water saturations

Electrically conductive particles, such as pyrites, and surface-charge-bearing nonconductive particles, such as clays, are commonly present in water-bearing subsurface formations. Under an external electric field generated by electromagnetic measurement tool, these particles give rise to interfacial polarization (IFP) effects, which causes frequency dispersion of effective conductivity and effective permittivity of the mixture containing such particles. The neglect of IFP effects can lead to inaccurate estimation of petrophysical properties of formations, especially in clay- and pyrite- rich formations. In this paper, we developed a mechanistic model that couples surface-conductance-assisted interfacial polarization (SCAIP) model with perfectly polarized interfacial polarization (PPIP) model to estimate effective conductivity and effective permittivity of homogeneous formations containing both nonconductive and conductive particles at various fluids saturations. The model is developed based on the Poisson-Nernst-Planck (PNP) equations for a dilute solution in a weak electrical field regime to calculate the dipolarizability of the representative volume comprising a single isolated spherical particle in an electrolyte host. Then the effective medium theory is used to determine effective complex conductivity of the whole mixture. The result shows that the conductive particles dominate the frequency dispersion of complex conductivity due to IFP effects compared to nonconductive particles.

Quantifying the effects of gaseous pore pressure and net confining stress on low-permeability cores using the “RaSSCAL” steady-state permeameter

Subsurface engineering systems undergo large changes in pore pressure, which can have drastic effects on their ability to transport and store fluids. For rock samples retrieved from conventional reservoirs, standard core analysis procedures provide suitable estimates of petrophysical properties, such as porosity and permeability. However, performing similar investigations on low-permeability systems like shales and tight gas sands remains a challenge. Steady-state gas permeametry is considered the gold standard for analyzing low-permeability cores, but researchers often forego it in favor of faster but less reliable methods. Attempting a conventional permeability test on a tight plug sample typically results in miniscule flow rates that only expensive, specialized equipment can measure directly. Furthermore, these time-consuming tests are best run multiple times under varying ranges of pore pressure and confining stress. Doing so allows one to anticipate suitably the changes in permeability that will occur because of the expected changes in reservoir conditions.This study describes the methodology of the Randolph Steady-State Core Analysis Laboratory (RaSSCAL), a new experimental design and data analysis algorithm that accounts for changes in permeability over the range of pore pressures and confining stresses that occur during processes like unconventional hydrocarbon recovery and geologic carbon sequestration. Routine steady-state core analysis requires relatively small pressure drops across the sample so a uniform apparent permeability can be assumed. The approach outlined here enables an effective interpretation of core-flood data in the presence of arbitrarily large pressure gradients. The experiment maintains a near-constant pressure difference of arbitrary magnitude across a core-plug sample and indirectly measures the flow rate induced by that gradient using a differential pressure transmitter. Tests were performed on a Marcellus Shale sample from Bedford, Pennsylvania and an Oriskany Sandstone sample from Berkeley Springs, West Virginia over a wide array of pore pressures and net confining stresses using helium and methane gas. Having a quantifiable understanding of how the permeability of the system may change during the various stages of subsurface activity should significantly enhance the ability to optimize design outcomes. This will ultimately lead to improved oil and gas recovery and better predictions of injectivity and storage capacity during carbon-storage operations.