Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data.
In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements.
The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.
Jin, Guodong (Baker Hughes, a GE Company) | Ali, Syed Shujath (Baker Hughes, a GE Company) | Al Dhamen, Ali A. (Baker Hughes, a GE Company) | Saad, Bilal (Baker Hughes, a GE Company) | Hussain, Maaruf G. (Baker Hughes, a GE Company) | Chinea, Gonzalo (Baker Hughes, a GE Company) | Nair, Asok (Baker Hughes, a GE Company) | Alshanqaiti, Elham (Baker Hughes, a GE Company)
ABSTRACT: This paper compared various unloading criteria used in multi-stage triaxial tests for determining the Mohr-Coulomb failure envelope. Results from single-stage triaxial tests formed the baseline for comparison. Three types of rock outcrop were used: Berea sandstone, Eagle Ford shale, and carbonate rock. Mineralogy, porosity, grain and bulk density were also measured, which are used to determine the sample heterogeneity and interpret the discrepancy of test results. Examples illustrated both zero and maximum volumetric strain criteria were not always applicable, especially for brittle rocks. The criterion of radial-strain gradient, defined as the ratio of change of radial strain and change of time, is generally suitable for any type of rocks. Irrespective of the applied confining pressure, samples were observed to break at almost the same radial-strain gradient for the same type of rocks. Failure envelopes from the radial-strain gradient method matched very well with those from the maximum volumetric strain for all samples tested. Compared to results of single-stage triaxial tests, multi-stage tests yielded a very good approximation of failure envelopes for Berea sandstone, while discrepancy was observed for Eagle Ford shale and carbonate rock because of the heterogeneity of samples.
Mohr-Coulomb failure envelope (hereafter referred to as only failure envelope for simplicity) is one of the most commonly used failure criteria in many engineering application, such as borehole instability analysis (Manshad et al., 2014), sand onset prediction (Javani et al., 2017), and mud-weight window design (Gholami et al., 2014). Determination of the failure envelope usually requires to perform several single-stage triaxial (SST) tests on three or more samples or one multi-stage triaxial (MST) test on one sample at various confining pressures. Due to the scarcity and preciousness of rock samples, MST test is often the only option used for determining the failure envelope (Harouaka et al., 1995).
One major challenge associated with MST tests is to recognize the imminent failure of the sample and thereby prevent failure of the sample from occurring at each loading stage except for last stage (Crawford and Wylie, 1987). Once the imminent failure is reached, the test should be stopped and unloaded to the confining pressure of that stage. Then, the confining pressure increases to the next level and the test of next stage starts. Theoretically the imminent failure is defined as the point in the stress- strain curve where the axial stress does not increase when the axial strain increases, or more specifically the point when the axial stress reaches at the peak stress (Kim and Ko, 1979, Kovári et al., 1983, Youn and Tonon, 2010). In practice, the peak stress of a sample at a given confining pressure is never known in advance, and therefore a subjective judgment must be made regarding the imminent failure. It is not uncommon that a wrong estimation often occurs when interpreting the stress-strain curves.
Rock mechanical properties are critical to reduce drilling risk and maximize well and reservoir productivity. This paper present a methodology of predicting mechanical properties (Young's modulus and Poisson's ratio) from 3D rock models generated using laboratory measurements or downhole logging data. The 3D rock model provides the microstructure and boundary to simulate rock elastic properties. Mechanical properties are computed from rock models using the finite element method.
Laboratory measurements were conducted on four Berea sandstone samples to determine the mechanical properties for comparison and other properties as input in rock modeling, such as bulk and grain density, porosity, mineralogy, and grain-size distributions. Numerical results from rock models generally match the core measurements of the corresponding samples. The methodology proposed in this study could potentially be applied downhole for predicting the mechanical property profile along the wellbore, as all input parameters to generate rock models can be derived from logging measurements.
The application of geomechanics has a significant impact on all aspects of field development - from the exploration and appraisal phases through development and harvest of the field, to the final abandonment. One optimized field development plan using geomechanics can result in very large cost savings over the life of the field. Geomechanical properties of reservoir rocks (Young's modulus and Poisson's ratio) are always required in various geomechanics applications (Yale et al., 1995). Their determination are usually from laboratory triaxial compression testing on a limited number of core samples. Test data of cores are commonly considered as the standard and used as a reference and calibrated value to build the geomechanical model. Because of cost, time and limited core availability, triaxial testing is difficult to conduct for each interval and each well. Alternatively, the static mechanical properties can be derived from the dynamic properties obtained from well logs using some correlation built on core data.
Successful exploitation of shale reservoirs largely depends on the effectiveness of hydraulic fracturing stimulation program. Favorable results have been attributed to intersection and reactivation of pre-existing fractures by hydraulically-induced fractures that connect the wellbore to a larger fracture surface area within the reservoir rock volume. Thus, accurate estimation of the stimulated reservoir volume (SRV) becomes critical for the reservoir performance simulation and production analysis. Micro-seismic events (MS) have been commonly used as a proxy to map out the SRV geometry, which could be erroneous because not all MS events are related to hydraulic fracture propagation. The case studies discussed here utilized a fully 3-D simulation approach to estimate the SRV.
The simulation approach presented in this paper takes into account the real-time changes in the reservoir's geomechanics as a function of fluid pressures. It is consisted of four separate coupled modules: geomechanics, hydrodynamics, a geomechanical joint model for interfacial resolution, and an adaptive re-meshing. Reservoir stress condition, rock mechanical properties, and injected fluid pressure dictate how fracture elements could open or slide. Critical stress intensity factor was used as a fracture criterion governing the generation of new fractures or propagation of existing fractures and their directions. Our simulations were run on a Cray XC-40 HPC system.
The studies outcomes proved the approach of using MS data as a proxy for SRV to be significantly flawed. Many of the observed stimulated natural fractures are stress related and very few that are closer to the injection field are connected. The situation is worsened in a highly laminated shale reservoir as the hydraulic fracture propagation is significantly hampered. High contrast in the in-situ stresses related strike-slip developed thereby shortens the extent of SRV. However, far field nature fractures that were not connected to hydraulic fracture were observed being stimulated.
These results show the beginning of new understanding into the physical mechanisms responsible for greater disparity in stimulation results within the same shale reservoir and hence the SRV. Using the appropriate methodology, stimulation design can be controlled to optimize the responses of in-situ stresses and reservoir rock itself.
The recent crash in the oil market has allowed the industry to reduce the pace of evaluation and completion decisions in unconventional reservoirs, and turn to a more science-based decision-making process for project execution. The traditional stimulation design based on the geometric spacing of induced fractures is now gradually changing to geological spacing (i.e., a design based on an understanding of the reservoir geology) to enhance hydraulic fracture stimulation effectiveness for drastically reduced cost. A methodical rock texture characterization of core samples and cuttings can provide powerful information that can be used reliably and cost-effectively to optimize fracture stimulation designs by placing frac stages based on rock characteristics. This paper presents a new method to quantify rock texture based on automated petrographic analysis that uses advanced microscopy image analysis from scanning electron microscopy (SEM) and optical microscopy. A procedure called "quantitative evaluation of minerals using a scanning electron microscope" (QEMSCAN) and optical microscopy analyses were used to image rock samples prepared from cores and cuttings. Rock texture parameters were extracted automatically using new digital data processing techniques. The information from automated petrographic analysis was used to determine the spatial distribution of all components including mineral composition, framework grains, matrix, cement, grain size and shape, pore size and shape, modes of contact between grains and the nature of porosity. The results showed that while mineral composition of rock is important, texture characterization is far more significant to understand rock behavior than has been reported in the industry. Our results demonstrate the importance of quantitative microscopy and how it can provide an understanding of the key relationship between rock texture and rock behavior.
A new method was produced to characterize rock texture quantitatively from advanced image analysis with the aid of an automated petrographic system.
Hussain, Maaruf (Baker Hughes) | Saad, Bilal (Baker Hughes) | Negara, Ardiansyah (Baker Hughes) | Elgassier, Mokhtar (Baker Hughes) | Agrawal, Gaurav (Baker Hughes) | Sun, Shuyu (University of Science & Technology) | Abdullah, King (University of Science & Technology)
It is often reported that around 60% of hydraulic fracturing stages are ineffective. If so, it is likely that the design accuracy is limited by the current state of modeling and hydraulic fracture (HF) simulations. Our study presents a new alternative – a full 3-D simulation with geomechanics coupled to fluid flow. With the conventional simulation, it is extremely hard to model opening of weak lamination (Lam) and nearly impossible to generate induced horizontal fractures against the vertical overburden stress. However, horizontal fractures are routinely evident in shale reservoirs as healed fractures observed along the bedding planes. Hence, the need and importance of a true 3-D simulator that could incorporate complex geology and dynamically simulate fracture propagation by accounting for realtime changes in geomechanics and fluid pressures. Case study uses shale reservoirs, which are heavily laminated with complex natural fractures (NFs). Numerical simulations consisted of four separate coupled modules - geomechanics, hydrodynamics, a geomechanical joint model for interfacial resolution, and an adaptive remeshing module. Reservoir stress condition, rock mechanical properties, and injected fluid pressure dictate how fracture elements could open or slide. Critical stress intensity factor was used as a fracture criterion governing the generation of new fractures or propagation of existing fractures and their directions. Simulation was run on a Cray XC-40 HPC system. Typical laminated shale reservoirs anisotropic geomechanical properties obtained from literature were used to estimate a 3-D geomechanical model and NF network. HF geometry was significantly different in the presence of weak bedding, compared to when bedding was strong enough to transmit crack tip stresses across the interface. Significant amounts of fracturing fluid can be diverted into creation of horizontal fractures, even when the pressure was below the vertical stress, once bedding discontinuities are activated. Choices of NF network and Lam thickness significantly affected observed fracture propagation. The value of 3-D modeling was clearly established. This method provides more accurate solutions for stimulation design optimization, e.g., landing points, number of stages, number of clusters, spacing between stages, and stimulated reservoir volume.
Production from unconventional reservoirs like shale gas has increased considerably in the past few years due to the advancement in twofold, i.e., horizontal drilling and hydraulic fracturing technologies. Although there has been some success in increasing gas production from shale reservoirs, unfortunately, the physicochemical processes that take place in the shale formations remain challenging and are not completely understood. Unlike conventional reservoirs, shale reservoirs are characterized by very small porosity and extremely low-permeability. Gas flow in this tight formation involves complex flow processes such as Knudsen diffusion, Klinkenberg effect, adsorption and desorption, strong rock-fluid interaction, rock deformation, etc. Furthermore, because of high pressure and high temperature reservoir conditions the gas behaves as real gas. In this work, our shale gas mathematical model is built based on the dual-porosity dual-permeability model that incorporates the complex flow processes mentioned above as well as the thermodynamic calculations. Peng-Robinson equation of state (PR-EOS) was used to calculate the gas density and compressibility factor by solving the cubic equation. In the numerical method implementation we combine the finite difference method with the experimenting pressure field approach to solve the pressure equations for the matrix and fracture systems in the dual-porosity dual-permeability model. This combination greatly reduces the computational cost when solving the large systems of pressure equations of the matrix and fracture. In this approach, a set of predefined pressure fields is generated in the solution domain such that the undetermined coefficients are calculated from these pressure fields. In the numerical example, we considered a shale reservoir with single production well. Comparison between real gas and ideal gas is studied and the result shows that considering the real gas behavior generates higher cumulative production, which implies that the gas transport capacity is higher than the ideal gas case. The result also indicates that considering real gas behavior in the model would increase the production and retard the decline curve. Therefore, it is very important to incorporate the real gas behavior into the model in order to be able to forecast the production accurately.
Minhas, Naeem-Ur-Rehman (Baker Hughes Inc.) | Saad, Bilal (Baker Hughes Inc.) | Hussain, Maaruf (Baker Hughes Inc.) | Nair, Asok J. (Baker Hughes Inc.) | Korvin, Gabor (King Fahd University of Petroleum and Minerals)
Though ‘Big Data’ has been a much talked topic in recent years, its potential has not been fully utilized to study rocks for the purpose of improving asset development workflow. Our research has been focused on this topic. Upstream research publications combining imaging; elemental analysis and the mineral compositional information to derive a mineral map have recently started. This is very welcome as both SEM (scanning electron) and Optical Microscopy have tremendous latent potential to assist in reservoir characterization including depositional environment and diagenesis and to develop a more accurate reservoir model. In this study we describe new advanced image analysis that combines both SEM and optical microscopy. Results are used to study rock texture and predict rock fracture behavior.
Carbonate and sandstone rock samples were imaged using QEMSCAN (Quantitative Evaluation of Minerals using Scanning Electron Microscope) and optical microscopy analysis. Rock sections were prepared from cores. New digital data processing techniques were devised to extract the information and compute statistics and eventually automate data extraction.
The information from image processing such as porosity, grain size, shape, mineral associations, average distance between the neighboring grains, spatial distribution, crack patterns etc. has been used to find correlations between crack propagation and the texture of the rock. Combination of SEM and optical imaging techniques allows one to differentiate between cement and the mineral grains. It is found that the crack pattern is affected by the number of mineral grains per unit area. Higher number of mineral grains per unit area leads to more complex crack pattern which has implications for fraccability. Results show that quantitative microscopy provides a relationship between rock texture and fracture behavior. A new mathematical model is developed to predict the crack length as a function of grain size.
While recently XRD/XRF and elemental composition have been more frequently used by Industry, this study focuses on the importance of accurate, comprehensive and quantitative rock texture characterization. Novel image processing techniques and workflows developed by the authors were used to quantify texture. This work also reinforces the case of using complementary microscopy techniques for more accurate and insightful analysis.
Saad, Bilal (Baker Hughes) | Negara, Ardiansyah (Baker Hughes) | Hussain, Maaruf (Baker Hughes) | Elgassier, Mokhtar (Baker Hughes) | Sun, Shuyu (University of Science and Technology) | Abdullah, King (University of Science and Technology)
Hydraulic fracture stimulation designs are typically made of multiple stages placed along the lateral section of the well using various well completion technologies. Understanding how multiple hydraulic fractures propagate and interact with each other is essential for an effective stimulation design. The number and placement of stages are important factors for optimizing the performance of the laterals. This in turn depends on accuracy in determining fracture interference. We present advanced simulations for accurate placement of well stages. In this paper, we use a 3-D fully coupled geomechanical-fluid flow simulator which incorporates anisotropic geomechanical properties. Densely complex natural fractures and lamination are built into the model based on available core and log information. Multiple fractures are concurrently imployed to simulate real life scenarios. Fluid pressures are incrementally computed such that stress state changes dynamically with time as it happens in real field situation. Our simulations were run on Cray XC 40 HPC system. The results demonstrate that the stress shadow effects can significantly alter hydraulic fracture propagation behavior, which eventually affects the final fracture geometry. The results show that there are large differences in aperture throughout the stimulation which persists to the end of pumping. Furthermore comparison between cases with and without complex natural fractures (discrete fracture network (DFN)) and lamination was conducted with even and uneven spacing configurations. Fracture interference and spacing analysis conducted based on model with perforation frictions shows that while spacing between fractures is important, the largest impact was observed in the presence of lamination and DFN. The large differences in the way the fracture propagates highly depend on the DFN connectivity. Late-stage connection throughout the model implies later disconnection when the pressure drops. Though the computations are time intensive, we believe this is a valuable tool to use in the planning stages for asset development to increase production potential.