Prediction Of Petrophysical Properties Research Articles

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Deterministic and Stochastic Modeling in Prediction of Petrophysical Properties of an Albian Carbonate Reservoir in the Campos Basin (Southeastern Brazil)

Abstract —Permeability is one of the most significant and challenging parameters to estimate when characterizing an oil reservoir. Several empirical methods with geophysical borehole logs have been employed to estimate it indirectly. They include the Timur model, which uses conventional logs, and the Timur–Coates model, which uses the nuclear magnetic resonance log. The first goal of this study was to evaluate porosity, because it directly impacts permeability estimates. Deterministic and stochastic inversions were then carried out, as the main objective of this work was to estimate the permeability in a carbonate reservoir of the Campos Basin, Southeastern Brazil. The ridge regression scheme was used to invert the Timur and Timur–Coates equations deterministically. The stochastic inversion was later solved using fuzzy logic as the forward problem, and the Monte Carlo method was utilized to assess uncertainty. The goodness of fit for the estimations was all checked with porosity and permeability laboratory data using the Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Willmott’s agreement index (d). The results for the Timur model were R = 0.41; RMSE = 333.28; MAE = 95.56; and d = 0.55. These values were worse for the Timur–Coates model, with R = 0.39; RMSE = 355.28; MAE = 79.35; and d = 0.51. The Timur model with flow zones had R = 0.55; RMSE = 210.88; MAE = 116.66; and d = 0.84, which outperformed the other two models. The deterministic inversion showed, thus, little ability to adapt to the significant variations of the permeability values along the well, as can be seen from comparing these three approaches. However, the stochastic inversion using three bins had R = 0.35; RMSE = 320.27; MAE = 190.93; and d = 0.73, looking worse than the deterministic inversion. In the meantime, the stochastic inversion with six bins successfully adjusted the set of laboratory observations, because it provides R = 0.87; RMSE = 156.81; MAE = 74.60; and d = 0.92. This way, the last approach has proven it can produce a reliable solution with consistent parameters and an accurate permeability estimation.

Probabilistic physics-informed neural network for seismic petrophysical inversion

The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties often are nonlinear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we develop a method to adopt machine-learning algorithms by estimating relations between data and unknown variables from a training data set with limited computational cost. We develop a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network (PINN) with a reparameterization network. The novelty of our approach includes the definition of a PINN algorithm in a probabilistic setting, the use of an additional neural network (NN) for rock-physics model hyperparameter estimation, and the implementation of approximate Bayesian computation to quantify the model uncertainty. The reparameterization network allows us to include unknown model parameters, such as rock-physics model hyperparameters. Our method predicts the most likely model of petrophysical variables based on the input seismic data set and the training data set and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea data set with poststack and prestack data to obtain the prediction of petrophysical properties. Compared with regular NNs, the predictions of our method indicate higher accuracy in the predicted results and allow us to quantify the posterior uncertainty.

Prediction of Inter-Well Petrophysical Properties from Seismic Model: A Case of Egbem Field, Niger Delta, Nigeria

We have generated a seismic model for the prediction of inter-well petrophysical properties of Egbem Field; in the Niger Delta, Nigeria. Acoustic Impedances (AI) were calculated from sonic and density logs in the available 24 wells. Reflection Coefficients (RC) determined from the acoustic impedances were convolved with modelled wavelet to produce synthetic seismograms at various well locations. A top structure map of a reservoir from the field was used to define the position of faults, which served as key pillars in gridding the reservoirs of the field into geocellular blocks of 50 x 50 meters each. Petrophysical analysis of the field showed an average porosity of 24.8% and volume of shale of 19.9%. Petrophysical parameters were geostatistically distributed on the generated framework model using Sequential Gaussian Simulation (SGS). The result of the Acoustic Impedance classification reveals four lithologic facies namely, shale, sandy shale, shaley sand and sand facies. The facies model indicates high and low acoustic impedances for sand and shale respectively. This model indicates that sand which is a good reservoir is concentrated in the central part of the field. The result of synthetic seismic generation showed a very good match between acoustic impedance and lithology. Comparison of porosity obtained from the porosity model with conventional well log porosity showed similarities in their histogram distribution. Finally, Three-dimensional seismic model of Egbem Field has been constructed using log data from twenty four wells. The application of geostatistical analyses extrapolated and interpolated these log data to cover the entire field.

A Deep Learning Framework for Petrophysical Properties Prediction in Gas Reservoirs

A Deep Learning Framework for Petrophysical Properties Prediction in Gas Reservoirs

Integrated workflow incorporating the Hurst exponent and interval inversion for evaluating groundwater formations

A novel well-log-analysis approach is presented for an improved prediction of petrophysical properties in groundwater formations. Geophysical well logs are simultaneously processed for quantifying the lithology, storage capacity, and water flow parameters. A fully automated data processing workflow is proposed, the feasibility of which is assured by an appropriate starting model set by the joint application of factor analysis and the Hurst exponent, and a solution of a highly overdetermined inverse problem. The Hurst exponent is used for zone boundary detection, which assists the series expansion-based interval inversion method applied for estimation of the petrophysical parameters of clastic formations. The hydraulic conductivity as a well log is directly derived from the inversion results. The workflow is tested using both synthetic data contaminated with 5% Gaussian distributed noise and real data collected from a thermal water well in Baktalórántháza, eastern Hungary. At the test site, the Hurst exponent extracted from the wireline logs allows one to divide the processed interval into subzones around the Pleistocene-Miocene boundary. The observed wireline logs are inverted to estimate the volumetric parameters (porosity, shale content, water saturation, etc.) of the same zones. The predicted parameters, including hydraulic conductivity, reveal that Pleistocene sediments contain good aquifers with formation quality varying with depth. The shale volume and hydraulic conductivity logs show a proper match with the core data, which confirms the results of the comprehensive analysis. The suggested workflow is recommended for the evaluation of groundwater formations located in different depth domains, from unsaturated sediments to geothermal reservoirs.

Curved Pseudo Elastic Impedance based on Elastic Attribute Rotation Scheme for Reservoir Petrophysical Property Prediction

Seismic based reservoir characterization commonly attempts to estimate petrophysical properties of a reservoir from a combination of elastic attributes resulted from seismic inversion. Two most wanted main reservoir properties to be estimated are porosity variation and hydrocarbon saturation across the reservoir. A combination of acoustic impedance (AI) and the ratio between P- and S-wave velocities (Vp/Vs ratio) based on rock physics models had been used in the earlier study to form Curved Pseudo Elastic Impedance (CPEI) attribute. However, the approach involved a fourth order polynomial in the logarithmic domain of AI and Vp/Vs ratio. A much simpler approach of combining these elastic properties to form CPEI attributes which is based on attribute rotation scheme is proposed in this study. To illustrate the methodology, we applied to well and seismic data located in the Natuna Basin, Indonesia. Results show that better attributes correlation to porosity and hydrocarbon saturation variation is achieved.

A data-driven method for total organic carbon prediction based on random forests

The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature variables and is very onerous and experience-dependent to derive effective feature variables from real seismic data. To address this issue, we propose to use the extended elastic impedance to automatically generate 222 extended elastic properties as the feature variables for RF predictor training. In addition, the synthetic minority oversampling technique is used to overcome the problem of RF training with imbalanced samples. With the help of variable importance measures, the feature variables that are important for TOC prediction can be preferentially selected and the redundancy of the input data can be reduced. The RF predictor is finally trained well for TOC prediction. The method is applied to a real dataset acquired over a shale gas study area located in southwest China. Examples illustrate the role of extended variables on improving TOC prediction and increasing the generalization of RF in prediction of other petrophysical properties.

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.

Decorrelated linearized seismic-petrophysics inversion

Seismic model and rock-physics models (RPMs) are of significance in petrophysics inversion to estimate petrophysical properties from seismic data. Nevertheless, the prediction of petrophysical properties is commonly restricted by two issues. One is that computationally intensive algorithms are required to invert petrophysical parameters coupled in most nonlinear RPMs. Another is that statistical error caused by correlation characterization between different petrophysical parameters can affect the accuracy of inverted results. To solve these two issues, a decorrelated linearized seismic-petrophysics inversion (DLSPI) based on a linearized seismic-petrophysics model is proposed. First, a novel linearized seismic-petrophysics model is constructed based on the Taylor first-order approximate RPMs including Nur's critical porosity model and Gassmann's equation. Next, a linearized seismic-petrophysics inversion (LSPI) is proposed under the linear Bayesian framework. This approach can estimate petrophysical parameters from seismic data directly and efficiently. Then, a decorrelation strategy based on principal component analysis (PCA) is introduced to the LSPI to form the DLSPI, which can avoid the effect of the statistical correlation between different petrophysical parameters and improve inversion accuracy. Finally, the proposed method is validated both on synthetic seismic data and real seismic data from a sandstone reservoir. The inverted results indicate the efficiency and stability of the proposed method.

Predicting Reservoir Petrophysical Geobodies from Seismic Data Using Enhanced Extended Elastic Impedance Inversion

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.

Petrophysical Charaterizations of Some Reservoir Formations in Ghani Oilfield, Libya

This study has been conducted on three types of reservoir rocks for core analysis of samples denoted by R-1, R-2 and R-3 of Ghani oil field from three reservoir include Farrud, Facha and Mabruk formations respectively. This analysis includes determination of physical characteristics e. g. porosity ( ), permeability (k), formation factor (FF) and resistivity index (RI). The purpose of this study is to how core petrophysical data might be most investigating effectively applied to the petrophysical prediction of petrophysical properties from core samples analysis. For Farrud reservoir of R-1 shows that the relation between degree of saturation ( ) and relative oil permeability(Kro) equal the relative permeability of water(Krw) at which the intersection point between the two curves ; whereas, the flow with the same rate. FF and RI are vary with and the RI is a function of . The gas-oil relative permeability have been expressed graphically. Whereas, the intersection point between the two curves (Kro) equal the (Krg), at which both oil and gas are flow with the same rate. A similar results were obtained from Facha reservoir, R-2. The petrophysical properties of core samples for R-3 of Mabruk Formation have been performed including , k, and . The comparison between the three reservoirs by correlation between the average values such as , k, grain density and RI; shows that the average of Mabruk reservoir k greater than the other two reservoirs, while the other properties seem to be close together.

Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience

Greenhouse gas emissions cause serious global climate change, and it is urgent to curb CO2 emissions. As the last-guaranteed technology to reduce carbon emissions, carbon capture and storage (CCS) is emerging and has the potential to become an important choice to mitigate the greenhouse effect in the future. Forward-looking project deployment and accurate forecasting techniques occupy an indispensable position in CCS. Machine learning (ML), one of the fastest developing intelligent technology fields at present era, is considered as a substantial means to realize forecast demand relying on computer science and data statistics. This work provided a comprehensive review of ML applications in CCS, based on classical ML methods and mainstream research directions in CCS. The study shown that ML algorithms such as artificial neural network (ANN) and convolutional neural network (CNN) were widely used, mainly for predicting physical properties, evaluating mechanical stability, and monitoring CO2 plume migration and leakage during CO2 storage. Support vector machine (SVM) was generally combined with other ML methods for the prediction of petrophysical properties and sensitivity analysis of influencing factors. Deep learning (DL) algorithms, represented by generative adversarial network (GAN) and long short-term memory (LSTM), had shown good results in real-time monitoring of CO2 migration and leakage. Decision tree (DT) and random forest (RF) were mainly used to establish risk assessment and decision analysis framework, and estimate the success probability of CCS. This review summarized the applications of ML algorithms in CCS and presented the challenges and future prospects, from the geoscience perspective. The findings of this work can help for better understanding of the key role played by ML in CCS, as well as guiding the selection of ML algorithms and the development of new models in CCS research.

Petrophysical inversion based on f-s-r amplitude-variation-with-offset linearization and canonical correlation analysis

The prediction of petrophysical properties, such as porosity and rock-fluid volumes, from partially stacked seismic data typically requires a rock-physics model that often is lithology dependent and difficult to calibrate. We adopt canonical correlation analysis (CCA) to infer the underlying relation between petrophysical properties and elastic attributes estimated from seismic data. We develop a two-step inversion approach: first, we predict elastic properties from partially stacked seismic data using a Bayesian linear inverse method based on an amplitude-variation-with-offset (AVO) linearization in terms of fluid, rigidity, and density factors, and then we predict petrophysical properties from the estimated AVO attributes using CCA. The novelty of our approach is the application of CCA to the fluid and rigidity factors, which avoids the calibration of an explicit rock-physics model by automatically deriving a linear relation in the lower dimensional space of the canonical variables. The parameterization of the linearization in terms of fluid, rigidity, and density factors maximizes the correlation with respect to the petrophysical properties of interest. Furthermore, the probabilistic approach is extended to the petrophysical inversion using Bayesian linear theory and the posterior distribution of petrophysical properties conditioned by seismic data is computed by combining the probability distributions obtained from seismic and petrophysical inversion to propagate the uncertainty from the seismic to the petrophysical domain. The inversion is validated on a synthetic case that finds high accuracy of our formulation. A case study with synthetic and real partially stacked seismic data also is presented and compared to a traditional inversion with an explicit rock-physics model.

Use of neural networks for dynamic interpretation of seismic data

Neural networks and machine learning have long been used by almost everyone in their daily lives, perhaps not always consciously. When an algorithm of social networks identifies the faces of people in a photo or a voice assistant helps us search for some information, machine learning techniques underpin all of these activities.
 In recent years neural networks are finding more and more applications in the fields of oil and gas exploration and production. This article aims to illustrate an example of the application of neural networks in the analysis of seismic data for an active oilfield by predicting 3D cube of petrophysical properties to further detail the geological model and search for additional hydrocarbon accumulations.
 One of the key conditions for successful prediction of petrophysical properties using neural networks is a wide sample of well data for effective training of a non-linear operator. In our case, since it is a producing field, there were more than 100 wells available, which fully meets the requirements of the algorithm. Another important condition for application of this technique is having high-quality well ties for the used wells, this step of the workflow will also be described within the article.
 A distinct feature of neural network analysis, in contrast to classical inversion, is that it does not use a seismic wavelet. The neural network automatically determines such an operator that best describes the correlation between several seismic traces in the wellbore area and the log curve. This feature reduces the analysis time and produces express results if the above mentioned conditions are met, which makes the neural network technique an effective tool for dynamic analysis of seismic data.

Correction: Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Correction: Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Petrophysical properties prediction of deep dolomite reservoir considering pore structure

Retrieving electrical and structural carbonate formation properties from measurements on crushed rock using a multidata inversion approach

SUMMARY Quantification understanding, and prediction of physical rock properties rely so far predominantly on laboratory analyses of cores and plugs. Based on such data, petrophysical models are found that relate both microstructural properties and environmental conditions to geophysically accessible quantities. When considering reactive rock–fluid–gas systems, for example in geothermal energy, enhanced oil recovery or carbon dioxide sequestration, especially with carbonatic rock matrix, this approach is costly and time-consuming at best, or impossible to implement at worst. This is based on the two following reasons: First, porosity, permeability and accessible internal surface area in solid rock plugs are often so low that experimental time duration of many months or even years would be required to achieve chemical equilibrium. Secondly, plugs are single specimens of their — generally heterogeneous — original rock formation, which strongly questions the representativeness of single-plug data. To overcome these shortcomings, we present a new methodology based on the combination of systematic crushing, multimethod laboratory measurements and model-based computational evaluation with solving an inverse problem. As a first step, a large amount of undisturbed rock is intentionally crushed and divided in several particle size classes. Then, petrophysical laboratory measurements are carried out on all particle size classes. The resulting data set is finally inverted for the intended properties of the undisturbed rock. This inverse problem entails a three-level forward model, which parametrizes the undisturbed rock properties, particle characteristics and particle packings, but can also be freely adapted to other tasks by any suitable model representation. The three-level model is designed to enforce the petrophysical correlation of all properties at all levels while using a minimal set of model parameters, thus keeping the inverse problem overdetermined. For the inversion, we have developed a publicly available software tool (AnyPetro) based on a Gauss–Newton inversion scheme to minimize a damped least-squares objective function. To demonstrate and validate the proposed methodology, we present a study using five rock types — four carbonates and one sandstone as a reference. Laboratory measurements of complex electrical conductivity (from spectral induced polarization), specific surface (from nitrogen adsorption) and intraparticle porosity (from mercury intrusion) have been carried out on eight particle size classes and on plugs of each rock for comparison. Supportive and complementary analyses include, for example particle geometry, nuclear magnetic resonance, scanning electron microscopy, computer tomography, uniaxial compression strength and mineralogical composition. We show that our new methodology is highly capable of robustly recovering the complex electrical conductivity, specific surface and porosity of the undisturbed rocks from the measured data. The resulting sets of model parameters are petrophysically reasonable and verifiable. The presented methodology can further be applied to the use of drill cuttings as sample material, which is often the only available rock material from deep wells. Our findings also represent a methodological advance for laboratory experiments on reactive systems and both the interpretation and prediction of petrophysical rock properties in such systems.

Siamese-SR: A Siamese Super-Resolution Model for Boosting Resolution of Digital Rock Images for Improved Petrophysical Property Estimation

Digital Rock Physics leverages advances in digital image acquisition and analysis techniques to create 3D digital images of rock samples, which are used for computational modeling and simulations to predict petrophysical properties of interest. However, the accuracy of the predictions is crucially dependent on the quality of the digital images, which is currently limited by the resolution of the micro-CT scanning technology. We have proposed a novel Deep Learning based Super-Resolution model called Siamese-SR to digitally boost the resolution of Digital Rock images whilst retaining the texture and providing optimal de-noising. The Siamese-SR model consists of a generator which is adversarially trained with a relativistic and a siamese discriminator utilizing Materials In Context (MINC) loss estimator. This model has been demonstrated to improve the resolution of sandstone rock images acquired using micro-CT scanning by a factor of 2. Another key highlight of our work is that for the evaluation of the super-resolution performance, we propose to move away from image-based metrics such as Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) because they do not correlate well with expert geological and petrophysical evaluations. Instead, we propose to subject the super-resolved images to the next step in the Digital Rock workflow to calculate a crucial petrophysical property of interest, viz. porosity and use it as a metric for evaluation of our proposed Siamese-SR model against several other existing super-resolution methods like SRGAN, ESRGAN, EDSR and SPSR. Furthermore, we also use Local Attribution Maps to show how our proposed Siamese-SR model focuses optimally on edge-semantics, which is what leads to improvement in the image-based porosity prediction, the permeability prediction from Multiple Relaxation Time Lattice Boltzmann Method (MRTLBM) flow simulations as well as the prediction of other petrophysical properties of interest derived from Mercury Injection Capillary Pressure (MICP) simulations.

Controlling Factors on Petrophysical and Acoustic Properties of Bioturbated Carbonates: (Upper Jurassic, Central Saudi Arabia)

Many of the world’s productive Jurassic reservoirs are intensively bioturbated, including the sediments of the Upper Jurassic Hanifa Formation. Hydrocarbon exploration and production from such reservoirs require a reliable prediction of petrophysical properties (i.e., porosity, permeability, acoustic velocity) by linking and assessment of ichnofabrics and trace fossils and determining their impact on reservoir quality. In this study, we utilized outcrop carbonate samples from the Hanifa Formation to understand the main controlling factors on reservoir quality (porosity and permeability) and acoustic velocity of bioturbated carbonates, by using thin-section petrography, SEM, XRD, CT scan, porosity, permeability, and acoustic velocity measurement. The studied samples are dominated by Thalassinoides burrows that have burrow intensity ranging from ~4% to 27%, with porosity and permeability values ranging from ~1% to 20%, and from 0.002 mD up to 1.9 mD, respectively. Samples with coarse grain-filled burrows have higher porosity (average µ = 14.44% ± 3.25%) and permeability (µ = 0.56 mD ± 0.55) than samples with fine grain-filled burrows (µ = 6.56% ± 3.96%, and 0.07 mD ± 0.16 mD). The acoustic velocity is controlled by an interplay of porosity, bioturbation, and mineralogy. Samples with relatively high porosity and permeability values (>10% and >0.1 mD) have lower velocities (<5 km/s) compared to tight samples with low porosities and permeabilities (<10% and <0.1 mD). The mineralogy of the analyzed samples is dominated by calcite (~94% of total samples) with some quartz content (~6% of total samples). Samples characterized with higher quartz (>10% quartz content) show lower velocities compared to the samples with lower quartz content. Bioturbation intensity, alone, has no control on velocity, but when combined with burrow fill, it can be easier to discriminate between high and low velocity samples. Fine grain-filled burrows have generally lower porosity and higher velocities (µ = 5.46 km/s) compared to coarse grain-filled burrows (µ = 4.52 km/s). Understanding the main controlling factor on petrophysical properties and acoustic velocity of bioturbated strata can enhance our competency in reservoir quality prediction and modeling for these bioturbated units.