Prediction Of Rock Properties Research Articles

Detection methods for strength deterioration and structural characteristics of fractured rock based on digital drilling

Fractures ubiquitously exist in strata, and large-scale fractures can cause low bearing capacity and poor stability in rock masses and can sometimes lead to hazards such as roof falls and collapses in mines and tunnels. The foundation of the control of surrounding rocks and effective prevention of such hazards is to be able to accurately determine the structural characteristics of fractures and strength parameters of rocks. Traditionally, testing is carried out mainly through indoor compression tests and borehole inspection, which require drilling of the surrounding rock on site and core testing indoors. The effective in situ and accurate detection of the structure and strength properties of surrounding rock has become a challenge. Digital drilling testing of rock mass is a new approach to solve the above problems and future research direction. In this study, a multifunctional rock mass digital drilling test system is used to perform tests on different types of fractured rocks. The response of drilling parameters to fracture parameters is analysed, and a strength deterioration evaluation index for fractured rock based on drilling parameters is established. Then, the response mechanism of the strength deterioration rate while drilling in the fracture zone is defined, and a model for identifying fracture parameters based on drilling is established. Furthermore, an in situ detection method for fractured rock properties based on digital drilling is proposed. The results of the validation test show that the average difference ratio between the equivalent compressive strength measured by the drilling test and the laboratory compressive test is 3.97 %. The strength degradation of the fractured rock is significant, with a maximum strength degradation rate of 82.1 % obtained through drilling. The average identification accuracies of the fracture position and width are 2.56 mm and 0.85 mm, respectively. The identification accuracy of rock properties while drilling is high, which proves the effectiveness of the prediction model while drilling. This study can provide a theoretical and methodological basis for the accurate in situ prediction of rock properties in underground engineering fields, such as mine roadways, transportation tunnels, and hydraulic tunnels.

Predicting the strength, density, and porosity of rocks from roll crusher tests

The density, porosity, and strength of rocks are fundamental in the design of geo-engineering projects. Determining these properties requires the preparation of smooth core samples, which is usually impossible for soft rocks. Besides, only fragmented samples may be available in some projects. A method for predicting rock properties from rock particles such as drilling debris would therefore be useful. This study was undertaken to develop prediction equations for compressive strength (UCS), tensile strength (BTS), density, and porosity values from the crushability index (CI) obtained from rock fragments. The results showed that UCS, BTS, density, and porosity were strongly correlated with the CI. The physical characteristics and strength of rocks may be predicted with the help of the derived equations. The CI test is a valuable tool for estimating rock properties in drilling projects and in situations where core specimens are not available.

Technology Focus: Drilling Automation and Innovation (February 2024)

In recent years, operators and service companies have optimized drilling activities and reduced operational costs by accelerating the deployment of digital solutions. Among other applications, predictive data analytics are commonly used to estimate rock properties, reduce operational uncertainty, improve equipment maintenance processes, and optimize scarce human resources on specific tasks. Not surprising, then, is the number of initiatives to maximize the value of data and new machine-learning models to understand complex operations and the different levels of drilling automation around the world. The selection of technical papers for this month highlights some interesting examples implemented in diverse geographies. Sustainability appears today as a key driver for innovation. The complex process of decarbonization of our industry can be accelerated with the potential larger-scale application of emerging solutions with proven tangible impact. For instance, rig and equipment automation are a reality, with the capacity to lower emissions in drilling operations and help obtain improved and more-consistent results. Papers SPE 216249 and IPTC 22975 illustrate examples of actual benefits in different projects. The task of collecting, processing, organizing, and making sense of drilling information from multiple sources with minimum time requirements remains an opportunity for improved decision-making in project management. Paper OTC 32978 describes one case with the use of artificial intelligence for automatic classification of daily drilling reports as an enabler for better planning and accurate risk analysis. Worth emphasizing is the importance of extended reality as a growing and promising alternative with untapped potential in well construction, mainly as a resource for active learning and a collaboration tool among multidisciplinary teams. Paper SPE 212532, included in the additional recommended reading, gives an interesting overview of current applications associated with drilling operations and challenges ahead. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217113 Machine-Learning Techniques for Real-Time Prediction of Essential Rock Properties While Drilling by K.W. Amadi, Australian University, et al. SPE 212532 Extended Reality and Gamification for Drilling by Crispin Chatar, SLB, et al. SPE 213043 Ensemble Machine Learning for Data-Driven Predictive Analytics of Drilling Rate of Penetration Modeling: A Case Study in a Southern Iraqi Oil Field by Dhuha T. Al-Sahlanee, BP, et al.

Machine Learning and Data Fusion Approach for Elastic Rock Properties Estimation and Fracturability Evaluation

Accurate rock elastic property determination is vital for effective hydraulic fracturing, particularly Young's modulus due to its link to rock brittleness. This study integrates interdisciplinary data for better predictions of elastic modulus, combining data mining, experiments, and calibrated synthetics. We used the microstructural insights extracted from rock images for geomechanical facies analysis. Additionally, the petrophysical data and well logs were correlated with shear wave velocity (Vs) and Young's modulus. We developed a machine-learning workflow to predict Young's modulus and assess rock fracturability, considering mineral composition, geomechanics, and microstructure. Our findings indicate that artificial neural networks effectively predict Young's modulus, while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies. Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment. Notably, fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals. In conclusion, this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability, aiding hydraulic fracturing design optimization through diverse data and advanced methods.

Influence of rock properties on thrust and torque during rock coring operations a case study

The most important parameters for rock strength evaluations in excavation, mining, and mechanical engineering projects are the uniaxial compressive strength, Brazilian tensile strength, density, abrasivity, and impact strength index. To estimate these parameters, several laboratory investigations and nondestructive methods were proposed. The possibility of using time-saving models for predicting uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density, abrasivity, and impact strength index was investigated because of the difficulties in core specimen preparation. The novelty of this investigation was the prediction of rock properties using thrust and torque generated during rock coring operations. For this purpose, drilling experiments were conducted on seven different types of rock samples using a computer numerical control drilling machine. Based on the statistical analysis, simple linear regression equations were proposed for estimating rock properties, such as compressive strength, tensile strength, density, abrasivity, and impact strength index. The coefficient of determination (R 2), values obtained for thrust are 90.8, 93.3, 80.7, 87.6, and 96.3%, for compressive strength, tensile strength, density, abrasivity, and impact strength index, respectively. Whereas the corresponding torque values are 84.9, 83.9, 95.9, 81.6, and 82.6%. The developed models can be used in the exploration and excavation developments for the prediction of rock properties.

Dynamic Compressive Mechanical Property Characteristics and Fractal Dimension Applications of Coal-Bearing Mudstone at Real-Time Temperatures

Coal-bearing rocks are inevitably exposed to high temperatures and impacts (rapid dynamic load action) during deep-earth resource extraction, necessitating the study of their mechanical properties under such conditions. This paper reports on dynamic compression tests conducted on coal-bearing mudstone specimens at real-time temperatures (the temperature of the rock remains constant throughout the impact process) ranging from 25 °C to 400 °C using a temperature Hopkinson (T-SHPB) test apparatus developed in-house. The objective is to analyze the relationship between mechanical properties and the fractal dimension of fractured fragments and to explore the mechanical response of coal-bearing mudstone specimens to the combined effects of temperature and impact using macroscopic fracture characteristics. The study found that the peak stress and dynamic elastic modulus initially increased and then decreased with increasing temperature, increasing in the 25–150 °C range and monotonically decreasing in the 150–400 °C range. Based on the distribution coefficients and fractal dimensions of the fractured fragments, it was found that the degree of damage of coal-bearing mudstone shows a trend of an initial decrease and then an increase with increasing temperature. In the temperature range of 25–150 °C, the expansion of clay minerals within the mudstone filled the voids between the skeletal particles, resulting in densification and decreased damage. In the temperature range of 150–400 °C, thermal stresses increased the internal fractures and reduced the overall strength of the mudstone, resulting in increased damage. Negative correlations between fractal dimensions, the modulus of elasticity, and peak stress could be used to predict rock properties in engineering.

Experimental study on predicting rock properties using sound level characteristics along the borehole during drilling

Experimental study on predicting rock properties using sound level characteristics along the borehole during drilling

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.

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.

Gas channel delineation utilizing a neural network and 3D seismic attributes: Simian Field, offshore Nile Delta, Egypt

Depositional architecture and hydrocarbon-bearing reservoir detection are considered the main challenges in the Nile Delta Basin, predominantly in the slope area. Channel fill deposits are very complicated because they are vertically stacked and pass through different depositional stages, forming complex depositional patterns. In the current study, an artificial neural network was used to detect gas and water-bearing reservoirs inside the complex depositional pattern and their spatial dimensions. Modern seismic reservoir characterization attribute analysis provides a good outcome in detecting reservoir fluids by mixing fluid-sensitive attributes with the well outcomes. Artificial Neural Network (ANN) algorithms for rock property prediction are considered to be a new emerging trend. The ANN algorithm has an advantage over other seismic reservoir delineation techniques because it has a good capability for building a nonlinear relationship between seismic data and petrophysical logs.Hence, it can be utilized to spatially anticipate reservoir fluids with a rational degree of accuracy. A back-error propagation algorithm, a supervised neural network method, was implemented on the Simian Field to anticipate the fluid-bearing reservoirs from the seismic-derived amplitudes. The ANN method showed unique results in the case study area by discriminating the gas-bearing intervals from the water-bearing intervals, although combining the assembly of the seismic attributes to supervise fluid classification. The method will help in the development plans of the field and track the volume of water and gas.

Effects of type and distribution of clay minerals on the physico-chemical and geomechanical properties of engineered porous rocks

The study of the properties of engineered rocks is of great importance to researchers in engineering sciences such as petroleum, mining, and civil engineering owing to their wide application in these fields. In the present study, a physico-chemical and geomechanical investigation was carried out on the effects of different clay minerals on porous rocks. Various chemical products formed during chemical interactions between cement, clay minerals, and water can change the pore structure and thus the rock characteristics. The results of the current study showed that increasing the clay content could remarkably reduce the porosity and permeability of the rock by an average of 86% and 6.76%, respectively. In this regard, samples containing kaolinite were further influenced due to their new pore structure. Moreover, a power relationship was found between sonic velocity and porosity, which can be used to predict rock properties. Chemical analysis indicated an amplification in quantities of chemical products, particularly calcium silicate hydrate and portlandite, due to an increase in clay content. The impacts of porosity and cementation quality as two main factors on rock strength have also been studied. The outcomes revealed that a reduction in porosity could compensate for detrimental effects of poor bond quality and consequently improved UCS by up to 30% in samples containing kaolinite, while decreasing the degree of cementation prevailed over the porosity reduction in specimens including illite and resulted in a 14% decrease in UCS. The effects of porosity and bond quality on UCS would cancel each other out in samples containing bentonite. It is worth noting that when it comes to changes in geomechanical characteristics, the dominant factor (i.e., porosity reduction or cementation quality) determines the ultimate effect of clay minerals on the properties of engineered porous rocks.

Smart Seismic Modeling - Artificial Intelligence in the Petroleum Industry

Vertical seismic profile (VSP) data have many known advantages, if being compared to seismic data such as broader bandwidth, less attenuation, clearer under well true depth (TD) image, multiple noise differentiation, shear wave information, direct attenuation and anisotropy measurements, higher fidelity amplitude versus offset (AVO) effect, etc. In this paper, we share the bottom-up approach that aims to maximize the information extracting from borehole seismic, in conjunction with seismic and well log data, to generate a geological model by integrating full wave-field modeling (FWM) to deep learning techniques, called smart seismic modeling (SSM) project. The study starts using data at well locations, with vertical seismic profile (VSP) forward modeling process. The 2D rock physics model is thus built from the prior well logs and the major interpreted seismic horizons and faults. By altering rock-physics properties values and thickness of each layer, the model has been multiplied thousand times. The rock-physics models and their simulated seismic traces are fed, as labels and inputs, respectively, into a 2D deep learning network. The network then extracts the non-linear relationships between inputs and labels so that thickness and rock-physics properties can simultaneously be regressed with least squared error loss function. Optimal trained weight set (artificial intelligence engine) is finally used to predict wellbore’s surrounding geologic structures and rock properties for all the wells in the study area. To make sure machine learning can provide a good work, similar training and prediction process is carried out from surface seismic forward modeling. Not only is it used for rock property prediction at the area apart from wells, but also for cross checking the prediction at well location. All 2D predicted rock-property models are merged by multi-point statistics (MPS) method using hard data from wells’ prediction and trends from actual VSP processing, such as anisotropy, 2D Qp, Qs, for a complete 3D geologic model. By doing with two different modeling processes, i.e., VSP forward modeling and surface seismic forward to cross check the results, results can obtain from this study are: 1)high quality static model for field development; 2)high quality dynamic model for field monitoring; 3)robust artificial intelligence engines for variety types of geology. In this study, we introduce the SMM approach and share promising successes in applying deep learning in rock property inversion process. The successful showcase has proved that deep learning technique can be used to extract any kind of rock properties from the big data of VSP, well log and of seismic.

Correlation of granite rock properties with longitudinal wave velocity in rock bolt

Physico-mechanical rock properties are typically investigated via laboratory tests using core samples. However, sample coring is time consuming and expensive. The longitudinal wave velocity in rock bolts embedded in a rock mass depends on the surrounding rock properties. Hence, the longitudinal wave velocity in rock bolts can be utilized to predict rock properties. This study presents the relationship between granite rock properties and longitudinal wave velocity ( v L ) in rock bolts to predict rock properties using the longitudinal wave velocity in a rock bolt. Chemical (saline solution) and mechanical (slake durability test) weathering processes are employed to diversify the properties of the rock specimens. Laboratory tests are conducted on rock specimens to measure the physico-mechanical rock properties, including the velocities ( v p and v s ) associated with constrained and shear moduli , density ( ρ ), Young's modulus ( E ), Poisson's ratio ( μ ), and porosity ( η ), compressive strength ( f c ), and slake durability index ( SDI ). The measured rock properties are used in the rock mass model, and variations in v L in the rock bolt with different properties are investigated via numerical simulations. Results show that v p , v s , ρ , E , and f c are correlated significantly with v L ( R 2 > 0.9), whereas η and SDI are moderately correlated with v L ( R 2 > 0.7). However, a meaningful correlation between μ and v L is not obtained. The root mean square and mean absolute percentage errors are estimated to validate the correlation equations, and errors are not considered. Results of the t -test show that the calculated t -value is higher than the critical t -value, and the p -value is smaller than the significance level of 0.05, indicating that the correlation coefficient is significant. This study shows that the velocity of longitudinal waves in a rock bolt can be a useful indicator for predicting in-situ rock properties.

Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks

Shear sonic logs are critical for formation evaluation, rock physics, quantitative reservoir characterization, and geomechanical studies. Although empirical and conventional machine learning (ML) have been used for shear sonic slowness estimates, both approaches suffer from multiple fundamental problems. New approaches to ML, namely, unsupervised multivariate time series clustering (Toeplitz inverse covariance-based clustering) and class-based ensemble ML, are integrated with geologic information and petrophysical inversion-based multimineral models to predict rock properties of the Wolfcamp Formation in the Midland Basin, United States. These new approaches consider the interdependence of wireline log attributes, temporal or depth dependence of log responses, multimodal geologic factors, and variability of individual ML model-based results to generate the best possible ML model. The results from the proposed approach indicate highly accurate and consistent shear sonic log predictions with a minimal error for thousands of feet (hundreds of meters) in several wells in the Wolfcamp Formation, covering a large study area in the basin. The results are used in estimating elastic properties, such as Poisson’s ratio, lambda-rho (incompressibility), and mu-rho (rigidity) to delineate brittle and ductile units in the formation.

Fault Rock Property Prediction on Jurassic Clastics of the Barents Sea, Norway

Summary We analyzed the fault rocks of a compartmentalized field in the Barents Sea, in an area with several tectonic elements, which formed at different tectonic events. Standard fault seal analysis (FSA) was conducted to predict the shale content of the fault rock (shale gouge ratio, SGR). A static cellular model based on well data, seismic data, and geological concepts served as input. The fault rock calibration workflow required various data acquired by different methods. We analyzed the Middle Triassic to Upper Jurassic clastic deposits to reconstruct the tectonic history. Apatite fission track (AFT) and (U-Th)/He thermochronology were used to determine the maximum burial depths and exhumation history. The results of high-resolution shale ductility analysis, a compaction trend study, kinematic analysis, and structural modeling (section balancing) served as additional input constraints for fault rock calibration. The interpretation of the results helped to reconstruct the following tectonic evolution. The orthogonal faults developed shortly after deposition, during Late Triassic to Early Jurassic times at relatively shallow depth, below 1000 m. Ongoing subsidence created accommodation space for Upper Jurassic to Cenozoic deposits with a maximum burial depth of 2000 m for the Middle Jurassic rocks. Exhumation of the area started around 10 Ma and continued through to Quaternary times. The predicted across-fault-flow values for fault rock permeability show a wide range when using poorly constrained input for fault rock calibration: 9.9E−15 to 9.9E−13 m² for SGR values around 0.08 at reservoir/reservoir juxtaposition. Fault rock calibration using elaborated results reduced the uncertainty of fault rock permeability estimates, and ultimately, for transmissibility multipliers (TMs). The reason for the sensitivity of the fault rock calibration is a combination of following factors: highly permeable reservoir sandstone, shallow depth of initial faulting, maximum burial depth and low shale content at the upper, main reservoir level. The study shows that an accurate reconstruction of the geohistory provides essential parameters for fault rock calibration and fault rock permeability prediction. The range of values can widely scatter if boundary conditions are not acknowledged. Well-constrained fault rock calibration reduces the uncertainty on possible flow scenarios, increases the reliability on production forecasts and helps determine the most efficient drainage strategy.

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.

Testing rebound hardness for estimating rock properties from core and wireline logs in mudrocks

Rebound hardness (RHN) has become a widely applied rock mechanical parameter in the petroleum industry due to economic and convenient testing procedures. However, the RHN data can be under-utilized when lacking detailed integration with other rock properties. Targeting the unconventional “Mississippian Limestone”/STACK play in north-central Oklahoma, USA, and outcrops of the Vaca Muerta Formation in Argentina, this study aims to test the value of RHN in predicting rock properties. RHN data from the “Mississippian Limestone”/STACK cores show correlative trends with mineralogy and porosity. All the correlations show clusters by facies groups with overlaps being present among different clusters. Within these correlations, mineralogy and porosity show variable significance levels in affecting RHN among different facies groups. Leverage analysis suggests that bulk clay content and porosity exhibits the most significant control on RHN for the MISS/STACK data, with variabilities being present in different facies groups. These partitioning patterns of data by facies groups imply that facies variability affects the statistical pattern and that RHN can assist in rock typing, and hence, sample selection for detailed laboratory analyses. Forward regression analysis reveals that the confidence level of predicting porosity and sonic velocity can be enhanced using RHN. In addition to the correlative trends between RHN and rock properties, results from forward regression analysis indicate that RHN can help estimate these properties in a faster, cheaper, and non-destructive way relative to conventional laboratory analyses. Correlative trends are also observed in Vaca Muerta data, suggesting the value of RHN in characterizing similar types of mixed carbonate-siliciclastic reservoirs. • Rebound hardness (RHN) can help estimate bulk clay content, porosity, sonic velocity, and dynamic elastic parameters. • RHN may help estimate static rock mechanical properties, but more data are needed for further investigation. • RHN cluster by facies and thus can help design an integrated rock typing and sample selection scheme. • Multivariate methods indicate RHN is affected by multiple factors and can help apply RHN to predict rock properties. • A facies-based, integrated approach with a well-designed testing protocol is critical for using RHN in reservoir analysis.

Geostatistical Seismic Rock Physics AVA Inversion With Data-Driven Elastic Properties Update

Geostatistical seismic rock physics amplitude-versus-angle (AVA) inversion allows the joint prediction of rock and fluid properties from seismic reflection data. In these seismic inversion methods, the model perturbation and update occur iteratively in the petrophysical domain. A facies-dependent precalibrated rock physics model is applied to the simulated rock properties to calculate elastic properties. Synthetic seismic reflection data are computed from these elastic models. The rock physics models are calibrated at the well locations and act as the link between the rock and the elastic domains, remaining unchanged during the inversion procedure: the convergence of the inversion and the geological plausibility of the inverted rock property models depend on the quality of the calibration and available well-log data. To overcome this limitation, account for calibration errors and geological scenarios not sampled at the well locations, we introduce an iterative geostatistical seismic AVA inversion where the elastic predictions from a facies-dependent precalibrated rock physics model are updated iteratively, based on the mismatch between observed and synthetic data. We assume an initial <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> <i>priori</i> uncertainty in the rock physics model and update it iteratively based on the data mismatch. The proposed method is applied in a 1-D synthetic example to illustrate the concept and validate the method. Then, we apply the inversion method to a 3-D real data set. In this application, we use a blind well to assess locally the performance of the technique. The results show that the parameters of space exploration is not compromised by the rock physics model&#x2019;s uncertainty.

Use of Seismic Spectral Decomposition, Phase, and Relative Geologic Age as Attributes to Improve Quantitative Porosity Prediction in the Daqing Field, China

The high production potential of the Daqing oilfield in China is recognized for seismically thin sand bodies that usually are not resolved with conventional seismic data. The present study assesses the usefulness of applying seismic multi-attribute analysis to bandwidth extended data in resolving and making inferences about these thin layers. In thin layers, tuning can obscure relationships between seismic amplitude and rock properties. In such cases, the seismic phase varies with the layer impedance and may hence aid in reservoir characterization. A seismically derived relative geologic age may also be a useful attribute in predicting rock properties because it helps define the stratigraphic position of a layer. When utilized in multi-attribute analysis in the Daqing field, spectral decomposition amplitude, phase, and a relative geological age attribute to improved prediction of well log effective porosity from seismic data and are preferentially selected by stepwise regression. The study follows standard methodology by implementing seismic multi-attribute analysis and discusses the improvement of applying it to bandwidth extended data. This will include a combination of attributes such as relative geologic age, phase, amplitude, and the magnitude components of spectrally decomposed data.

Ultrasonic prediction of crack density using machine learning: A numerical investigation

Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity (UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning (ML) for analyzing wave signals and predict rock properties such as crack density (CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid (RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2 > 96% with mean square error (MSE) < 25e-4 (normalized values). Overall, we found that: (i) performance of both CNN and LSTM is highly promising, (ii) accuracy of the transmitted signals is slightly higher than the reflected signals, (iii) accuracy of 2D signals is marginally higher than 1D signals, (iv) accuracy of horizontal and vertical component signals are comparable, (v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.