Recent Submissions

  • Carbon dioxide thickening: A review of technological aspects, advances and challenges for oilfield application

    Pal, Nilanjan; Zhang, Xuan; Ali, Muhammad; Mandal, Ajay; Hoteit, Hussein (Fuel, Elsevier BV, 2022-01) [Article]
    The relatively low density and viscosity of carbon dioxide (CO2) in supercritical state create several drawbacks, including gravity override, viscous fingering, water production/treatment problems, and poor proppant transport for the petroleum industry. The introduction of CO2 thickeners offers a promising additive technology with sufficient solubility and viscosity enhancement attributes. The current article reviews the technical advances, challenges, and applicability of thickened CO2, particularly for hydrocarbon recovery. Different types of thickeners, including polymers, tailor-made surfactants, and small associating compounds, were investigated in terms of their nature, physicochemical traits, cost, and applications. The molecular weight and concentration, shear rate, co-solvent composition, temperature, and pressure play a significant role in the intermolecular forces and miscibility effect of thickeners in the presence of dense CO2. Binary co-polymers (non-fluorinated non-siloxane materials) and small molecule (associating) compounds are promising options for CO2 thickening owing to their enhanced performance, cost-effectiveness, and low ecological footprint. This study provides a comprehensive review of existing technologies, outline the gaps, potential, and required area for improvement.
  • Fractal and multifractal characterization of stochastic fracture networks and real outcrops

    Zhu, Weiwei; Lei, Gang; He, Xupeng; Patzek, Tadeusz; Wang, Moran (Journal of Structural Geology, Elsevier BV, 2022-01) [Article]
    The fractal dimension and multifractal spectrum are widely used to characterize the complexity of natural fractures. However, systematic investigations, considering impacts of different fracture geometrical properties (fracture lengths, orientations, center positions) and system sizes, on the fractal and multifractal characterization of complex fracture networks are insufficient. Here, we utilize an in-house developed DFN modeling software, hatchfrac, to construct stochastic fracture networks with prescribed distributions and systematically study the impact of three geometrical properties of fractures and system sizes on the fractal and multifractal characterization. We calculate the single fractal dimension and multifractal spectrum with the box-counting method. The single fractal dimension, D, and the difference of singularity exponent, Δα, are used to represent the fractal and multifractal patterns, respectively. We find that fracture lengths, orientations and system sizes positively correlate with D and Δα, while the system size has the most significant impact among the four parameters. D is uncorrelated with fracture positions (FD), which means that a single fractal dimension cannot capture the complexity caused by clustering effects. However, Δα has a strong negative correlation with FD, implying that clustering effects make fracture networks more complex, and Δα can capture the difference. We also digitize 80 outcrop maps with a novel fracture detection algorithm and calculate their fractal dimension and multifractal spectrum. We find wide variations of D and Δα on those outcrop maps, even for outcrops at similar scales, indicating that a universal indicator for characterizing fracture networks at different scales or the same scale is almost impossible. D and Δα have negligible correlations with scales, supporting the self-similarity patterns of natural fracture networks.
  • Chemical Compositions in Modified Salinity Waterflooding of Calcium Carbonate Reservoirs: Experiment

    Yutkin, Maxim; Radke, C. J.; Patzek, Tadeusz (Transport in Porous Media, Springer Science and Business Media LLC, 2022-01-01) [Article]
    Modified or low-salinity waterflooding of carbonate oil reservoirs is of considerable economic interest because of potentially inexpensive incremental oil production. The injected modified brine changes the surface chemistry of the carbonate rock and crude oil interfaces and detaches some of adhered crude oil. Composition design of brine modified to enhance oil recovery is determined by labor-intensive trial-and-error laboratory corefloods. Unfortunately, limestone, which predominantly consists of aqueous-reactive calcium carbonate, alters injected brine composition by mineral dissolution/precipitation. Accordingly, the rock reactivity hinders rational design of brines tailored to improve oil recovery. Previously, we presented a theoretical analysis of 1D, single-phase brine injection into calcium carbonate-rock that accounts for mineral dissolution, ion exchange, and dispersion (Yutkin et al. in SPE J 23(01):084–101, 2018. Here, we present the results of single-phase waterflood-brine experiments that verify the theoretical framework. We show that concentration histories eluted from Indiana limestone cores possess features characteristic of fast calcium carbonate dissolution, 2:1 ion exchange, and high dispersion. The injected brine reaches chemical equilibrium inside the porous rock even at injection rates higher than 3.5 × 10−3 m s−1 (1000 ft/day). Ion exchange results in salinity waves observed experimentally, while high dispersion is responsible for long concentration history tails. Using the verified theoretical framework, we briefly explore how these processes modify aqueous-phase composition during the injection of designer brines into a calcium-carbonate reservoir. Because of high salinity of the initial and injected brines, ion exchange affects injected concentrations only in high surface area carbonates/limestones, such as chalks. Calcium-carbonate dissolution only affects aqueous solution pH. The rock surface composition is affected by all processes.
  • Fluids, soils and rocks: Electromagnetic and NMR signatures

    Hakiki, Farizal; Santamarina, Carlos (Society of Exploration Geophysicists, 2021-12-24) [Conference Paper]
    Electromagnetic phenomena support the development of exceptional methods for non-destructive, non-contact geomaterial characterization and subsurface process monitoring. Such phenomena include Ohmic conduction, electromagnetic wave propagation and nuclear magnetic resonance. The physical parameters involved include DC conductivity σ, complex permittivity ε*(ω) and complex permeability μ*(ω) spectra, and nuclear magnetic resonance relaxation. These parameters reflect mineralogy, particle and pore size distributions, pore network topology and anisotropy, fluid composition, state and fluid-mineral interaction. Proper data interpretation requires careful understanding of underlying physical concepts and measurement methods.
  • Field Assessment of Camera Based Drilling Dynamics

    Koulidis, Alexis; Abdullatif, Mohamed; Abdel-Kader, Ahmed Galal; Ayachi, Mohammed Ilies; Ahmed, Shehab; Gooneratne, Chinthaka; Magana-Mora, Arturo; Affleck, Mike; Alsheikh, Mohammed (SPE, 2021-12-15) [Conference Paper]
    Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.
  • Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

    Santoso, Ryan; He, Xupeng; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.
  • Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation

    Santoso, Ryan; He, Xupeng; Alsinan, Marwa; Figueroa Hernandez, Ruben; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LHS) to construct the fine low-fidelity model to be deployed in the Bayesian runs, where we use the Metropolis-Hastings algorithm. Finally, the posterior is fed into the high-fidelity model to evaluate the matching quality. This work demonstrates the importance of including uncertainties in history matching. Bayesian provides a robust framework to allow uncertainty quantification within the reservoir history matching. Under uniform prior, the convergence of the Bayesian is very sensitive to the parameter ranges. When the solution is far from the mean of the parameter ranges, the Bayesian introduces bios and deviates from the observed data. Our results show that updating the prior from the coarse low-fidelity model accelerates the Bayesian convergence and improves the matching convergence. Bayesian requires a huge number of runs to produce an accurate posterior. Running the high-fidelity model multiple times is expensive. Our workflow tackles this problem by deploying a fine low-fidelity model to represent the high-fidelity model in the main runs. This fine low-fidelity model is fast to run, while it honors the physics and accuracy of the high-fidelity model. We also use ANOVA sensitivity analysis to measure the importance of each parameter. The ranking gives awareness to the significant ones that may contribute to the matching accuracy. We demonstrate our workflow for a geothermal reservoir with static and operational uncertainties. Our workflow produces accurate matching of thermal recovery factor and produced-enthalpy rate with physically-consistent posteriors. We present a novel workflow to account for uncertainty in reservoir history matching involving multi-resolution interaction. The proposed method is generic and can be readily applied within existing history-matching workflows in reservoir simulation.
  • High-Resolution Micro-Continuum Approach to Model Matrix-Fracture Interaction and Fluid Leakage

    He, Xupeng; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Understanding the fundamental mechanism of fracture-matrix fluid exchange is crucial for the modeling of fractured reservoirs. Traditionally, high-resolution simulations for flow in fractures often neglect the matrix-fracture leakage influence on the fracture hydraulic properties, i.e., assuming impermeable fracture walls. This work introduces a micro-continuum approach to capture the matrix-fracture leakage interaction and its effect on the rock fractures’ hydraulic properties. Because of the multiscale nature of fractured media, full physics Navier-Stokes (NS) representation everywhere in the whole domain is not feasible. We thus employ NS equations to describe the flow in the fracture, and Darcy’s law to model the flow in the surrounding porous rocks. Such hybrid modeling is achieved using the extended Darcy-Brinkman-Stokes (DBS) equation. With this approach, a unified conservation equation for flow in both media is applied by choosing appropriate parameters (e.g., porosity and permeability) for the corresponding domains. We apply an accurate Mixed Finite Element approach to solve the extended DBS equation. Various sensitivity analyses are conducted to explore the leakage effects on the fracture hydraulic properties by varying surrounding matrix permeability, fracture roughness, and Reynolds number (Re). Streamline profiles show the presence of back-flow phenomena, where in-flow and out-flow are possible between the matrix and the fractures. Further, zones of stagnant (eddy) flow are observed around locations with large asperities of sharp corners under high Re conditions. Numerical results show the significant effects of roughness and inertia on flow predictions in fractures for both impermeable and leaky wall cases. Besides, the side-leakage effect can create non-uniform flow behavior within the fracture that may differ significantly from the case with impermeable wall conditions. And this matrix-fracture leakage influence on hydraulic properties of rock fractures matters especially for cases with high matrix permeability, high fracture roughness, and low Re values. In summary, we present a high-resolution micro-continuum approach to explore the flow exchange behavior between the fracture and rock matrix, and further investigate the static and dynamic effects, including variable Reynold numbers, mimicking flow near and away from the wellbore. The approach and results provide significant insights into the flow of fluids through fractures within permeable rocks and can be readily applied in field-scale reservoir simulations.
  • An Accurate Cubic Law for the Upscaling of Discrete Natural Fractures

    He, Xupeng; AlSinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Abstract Modeling fluid flow in fractured reservoirs requires an accurate evaluation of the hydraulic properties of discrete fractures. Full Navier-Stokes simulations provide an accurate approximation of the flow within fractures, including fracture upscaling. However, its excessive computational cost makes it impractical. The traditionally used cubic law (CL) is known to overshoot the fracture hydraulic properties significantly. In this work, we propose an alternative method based on the cubic law. We first develop geometric rules based on the fracture topography data, by which we subdivide the fracture into segments and local cells. We then modify the aperture field by incorporating the effects of flow direction, flow tortuosity, normal aperture, and local roughness. The approach is applicable for fractures in 2D and 3D spaces. This paper presented almost all existing CL-based models in the literature, which include more than twenty models. We benchmarked all these models, including our proposed model, for thousands of fracture cases. High-resolution simulations solving the full-physics Navier-Stokes (NS) equations were used to compute the reference solutions. We highlight the behavior of accuracy and limitations of all tested models as a function of fracture geometric characteristics, such as roughness. The obtained accuracy of the proposed model showed the highest for more than 2000 fracture cases with a wide range of tortuosity, roughness, and mechanical aperture variations. None of the existing methods in the literature provide this level of accuracy and applicability. The proposed model retains the simplicity and efficiency of the cubic law and can be easily implemented in workflows for reservoir characterization and modeling.
  • Novel Analytical Solution and Type-Curves for Lost-Circulation Diagnostics of Drilling Mud in Fractured Formation

    Albattat, Rami; Hoteit, Hussein (SPE, 2021-12-15) [Conference Paper]
    Loss of circulation is a major problem that often causes interruption to drilling operations, and reduction in efficiency. This problem often occurs when the drilled wellbore encounters a high permeable formation such as faults or fractures, leading to total or partial leakage of the drilling fluids. In this work, we present a novel semi-analytical solution and type-curves that offer a quick and accurate diagnostic tool to assess the lost-circulation of Herschel-Bulkley fluids in fractured media. Based on the pressure and mud loss trends, the tool can estimate the effective fracture conductivity, the cumulative mud-loss volume, and the leakage period. The behavior of lost-circulation into fractured formation can be assessed using analytical methods that can be deployed to perform flow diagnostics, such as the rate of fluid leakage and the associated fracture hydraulic properties. In this study, we develop a new semi-analytical method to quantify the leakage of drilling fluid flow into fractures. The developed model is applicable for non-Newtonian fluids with exhibiting yield-power-law, including shear thickening and thinning, and Bingham plastic fluids. We propose new dimensionless groups and generate novel dual type-curves, which circumvent the non-uniqueness issues in trend matching of type-curves. We use numerical simulations based on finite-elements to verify the accuracy of the proposed solution, and compare it with existing analytical solutions from the literature. Based on the proposed semi-analytical solution, we propose new dimensionless groups and generate type-curves to describe the dimensionless mud-loss volume versus the dimensionless time. To address the non-uniqueness matching issue, we propose, for the first time, complimentary derivative-based type-curves. Both type-curve sets are used in a dual trend matching, which significantly reduced the non-uniqueness issue that is typically encountered in type-curves. We use data for lost circulation from a field case to show the applicability of the proposed method. We apply the semi-analytical solver, combined with Monte-Carlo simulations, to perform a sensitivity study to assess the uncertainty of various fluid and subsurface parameters, including the hydraulic property of the fracture and the probabilistic prediction of the rate of mud leakage into the formation. The proposed approach is based on a novel semi-analytical solution and type-curves to model the flow behavior of Herschel-Bulkley fluids into fractured reservoirs, which can be used as a quick diagnostic tool to evaluate lost-circulation in drilling operations.
  • Internet of Things IoT Edge Computer Vision Systems on Drilling Rigs

    Alsheikh, Mohammed; Gooneratne, Chinthaka; Magana-Mora, Arturo; Ibrahim, Mohamad; Affleck, Mike; Contreras, William; Zhan, Guodong David; Jamea, Musab Al; Umairin, Isa Al; Zaghary, Ahmed; Ayachi, Mohammed Ilies; Abdelkader, Ahmed Galal; Ahmed, Shehab; Makowski, Greg; Kapoor, Hitesh (SPE, 2021-12-15) [Conference Paper]
    This study focuses on the design and infrastructure development of Internet-of-Things (IoT) edge platforms on drilling rigs and the testing of pilot IoT-Edge Computer Vision Systems (ECVS) for the optimization of drilling processes. The pilot technology presented in this study, Well Control Space Out System (WC-SOS), reduces the risks associated with hydrocarbon release during drilling by significantly increasing the success and time response for shut-in a well. Current shut-in methods that require manual steps are prone to errors and may take minutes to perform, which is enough time for an irreversible escalation in the well control incident. Consequently, the WC-SOS enables the drilling rig crew to shut-in a well in seconds. The IoT-ECVS deployed for the WC-SOS can be seamlessly expanded to analyze drillstring dynamics and drilling fluid cuttings/solids/flow analysis at the shale shakers in real-time. When IoT-ECVSs communicate with each other, their value is multiplied, which makes interoperability essential for maximizing benefits in drilling operations.
  • Micromechanics of Drilling: A Laboratory Investigation of Formation Evaluation at the Bit

    Koulidis, Alexis; Mohamed, Fahd Mohamed; Ahmed, Shehab (SPE, 2021-12-15) [Conference Paper]
    Challenging drilling applications and low oil prices have created a new emphasis on innovation in the industry. This research investigates the value of drill bit based force sensing at the rock-cutter interface. For this purpose, a laboratory-based mini-rig has been built in order to recreate a scaled drilling process. The work aims to build a better understanding of the collected force and torque data despite the semi-continuous drilling process. This data is then used to estimate the formation strength. A scaled drill bit with two cutters was designed with sensors integrated into the drill bit cutter, drill string and the mini-rig structure. The mini-rig design allowed the accurate control of depth of cut by utilizing a comprehensive data acquisition and control system during the experiments. Initially, fifty-five samples were prepared with various water/gypsum ratios for a uniaxial compression test, scratch test, and for testing in the mini-rig. Prior to the mini-rig experiments, the results of the uniaxial compression and scratch tests were used as a benchmark to extract rock properties and the state of stress behavior. The experiments under atmospheric conditions revealed that the mini-rig could accurately estimate formation strength from a few rotations. The force data at the bit-rock interface was correlated with the torque measurements, and the results indicate that the tangential force has similar trends and relatively similar values. The groove created by the drill bit's rotating trajectory has a 14.45 cm circumference. This allows for a significant amount of data to be captured from a single rotation. The circular cutter geometry's influence is crucial for a continuous process since the active cutting area is continuously changing due to the pre-cut groove. The performed depth of cuts ranged from 0.1 to 1 mm in the same groove, and thus the active cutting area can be accurately calculated in real-time while conducting the experiments. Tangential and normal force data from the scratch test was analyzed in order to provide insights for correlation with the mini-rig data. The analysis shows that both tests give similar trends to the force measurements from the mini-rig. Moreover, the benchmark value of formation strength that was obtained from the uniaxial compression test was also in the same range. This illustrates the potential viability of drill bit based formation strength measurement due to the similarity between mini-rig test results and those using more classical testing practices. The experimental setup can provide a continuous cutting process that allows an accurate estimation of formation strength during a semi-continuous drilling operation with analogous application in the field. This can lead to an in-depth understanding of drilled formation properties while drilling and possibly assist in evaluating cutter wear state in-situ.
  • Application of a Drilling Simulator for Real-Time Drilling Hydraulics Training and Research

    Skenderija, Jelena; Koulidis, Alexis; Kelessidis, Vassilios; Ahmed, Shehab (SPE, 2021-12-15) [Conference Paper]
    Challenging wells require an accurate hydraulic model to achieve maximum performance for drilling applications. This work was conducted with a simulator capable of recreating the actual drilling process, including on-the-fly adjustments of the drilling parameters. The paper focuses on the predictions of the drilling simulator's pressure losses inside the drill string and across the open-hole and casing annuli applying the most common rheological models. Comparison is then made with pressure losses from field data. Drilling data of vertical and deviated wells were acquired to recreate the actual drilling environment and wellbore design. Several sections with a variety of wellbore sizes were simulated in order to observe the response of the various rheological models. The simulator allows the input of wellbore and bottom-hole assembly (BHA) sizes, formation properties, drilling parameters, and drilling fluid properties. To assess the hydraulic model's performance during drilling, the user is required to input the drilling parameters based on field data and match the penetration rate. The resulting simulator hydraulic outputs are the equivalent circulation density (ECD) and standpipe pressure (SPP). The simulator's performance was assessed using separate simulations with different rheological models and compared with actual field data. Similarities, differences, and potential improvements were then reported. During the simulation, the most critical drilling parameters are displayed, emulating real-time measured values, combined with the pore pressure, wellbore pressure, and fracture pressure graphs. The simulation results show promise for application of real-time hydraulic operations. The simulated output parameters, ECD and SPP, have similar trends and values with the values from actual field data. The simulator's performance shows excellent matching for a simple BHA, with decreasing system's accuracy as the BHA design becomes more complex, an area of future improvement. The overall approach is valid for non-Newtonian drilling fluid pressure losses. The user can observe the output parameters, and by adding a benchmark safety value, the simulator gives a warning of a potential fracture of the formation or maximum pressure at the mud pumps. Thus, by simulating the drilling process, the user can be trained for the upcoming drilling campaign and reach the target depth safely and cost-effectively during actual drilling. The simulator allows emulation of real-time hydraulic operations when drilling vertical and directional wells, albeit with a simple BHA for the latter. The user can instantly observe the output results, which allows proper action to be taken if necessary. This is a step towards real-time hydraulic operations. The results also indicate that the simulator can be used as an excellent training tool for professionals and students by creating wellbore exercises that can cover different operating scenarios.
  • Optimization and uncertainty quantification model for time-continuous geothermal energy extraction undergoing re-injection

    Hoteit, Hussein; He, Xupeng; Yan, Bicheng; Vahrenkamp, Volker (arXiv, 2021-12-10) [Preprint]
    Geothermal field modeling is often associated with uncertainties related to the subsurface static properties and the dynamics of fluid flow and heat transfer. Uncertainty quantification using simulations is a useful tool to design optimum field-development and to guide decision-making. The optimization process includes assessments of multiple time-dependent flow mechanisms, which are functions of operational parameters subject to subsurface uncertainties. This process requires careful determination of the parameter ranges, dependencies, and their probabilistic distribution functions. This study presents a new approach to assess time-dependent predictions of thermal recovery and produced-enthalpy rates, including uncertainty quantification and optimization. We use time-continuous and multi-objective uncertainty quantification for geothermal recovery, undergoing a water re-injection scheme. The ranges of operational and uncertainty parameters are determined from a collected database, including 135 geothermal fields worldwide. The uncertainty calculation is conducted non-intrusively, based on a workflow that couples low-fidelity models with Monte Carlo analysis. Full-physics reservoir simulations are used to construct and verify the low-fidelity models. The sampling process is performed with Design of Experiments, enhanced with space-filling, and combined with analysis of covariance to capture parameter dependencies. The predicted thermal recovery and produced-enthalpy rates are then evaluated as functions of the significant uncertainty parameters based on dimensionless groups. The workflow is applied for various geothermal fields to assess their optimum well-spacing in their well configuration. This approach offers an efficient and robust workflow for time-continuous uncertainty quantification and global sensitivity analysis applied for geothermal field modeling and optimization.
  • Solvable model of gas production decline from hydrofractured networks

    Marder, M.; Eftekhari, Behzad; Patzek, Tadeusz (Physical Review E, American Physical Society (APS), 2021-12-09) [Article]
    We address questions that arose from studying gas and oil production from hydrofractured wells. Does past production predict the future? This depends on deducing from production as much as possible about the plausible geometries of the fracture network. We address the problem through a solvable model and use kinetic Monte Carlo and Green's function techniques to solve it. We have three main findings. First, at sufficiently long times, the production from all compact fracture networks is described by a universal function with two scaling parameters, one of which is the diffusivity of unbroken rock α and the second of which is a parameter Vext with units of volume. Second, for fracture networks where the power-law distribution of fracture spacings falls below a critical value (and this appears to be the case in practice), early-time production always scales as one over the square root of time. Third, the diffusivity α that sets the scale for late-time production is inherently difficult to estimate from production data, but the methods here provide the best hope of obtaining it and thus can determine the physics that will govern the decline of unconventional gas and oil wells.
  • Discrete Fracture Model for Hydro-Mechanical Coupling in Fractured Reservoirs

    He, Xupeng; Qiao, Tian; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2021-12-09) [Conference Paper]
    The process of coupled flow and mechanics occurs in various environmental and energy applications, including conventional and unconventional fractured reservoirs. This work establishes a new formulation for modeling hydro-mechanical coupling in fractured reservoirs. The discrete-fracture model (DFM), in which the porous matrix and fractures are represented explicitly in the form of unstructured grid, has been widely used to describe fluid flow in fractured formations. In this work, we extend the DFM approach for modeling coupled flow-mechanics process, in which flow problems are solved using the multipoint flux approximation (MPFA) method, and mechanics problems are solved using the multipoint stress approximation (MPSA) method. The coupled flow-mechanics problems share the same computational grid to avoid projection issues and allow for convenient exchange between them. We model the fracture mechanical behavior as a two-surface contact problem. The resulting coupled system of nonlinear equations is solved in a fully-implicit manner. The accuracy and generality of the numerical implementation are accessed using cases with analytical solutions, which shows an excellent match. We then apply the methodology to more complex cases to demonstrate its general applicability. We also investigate the geomechanical influence on fracture permeability change using 2D rock fractures. This work introduces a novel formulation for modeling the coupled flow-mechanics process in fractured reservoirs, and can be readily implemented in reservoir characterization workflow.
  • Design and multi-objective optimization of a 12-slot/10-pole integrated OBC using magnetic equivalent circuit approach

    Metwly, Mohamed Y.; Hemeida, Ahmed; Abdel-Khalik, Ayman S.; Hamad, Mostafa S.; Ahmed, Shehab (Machines, MDPI AG, 2021-12-01) [Article]
    Permanent magnet machines (PMs) equipped with fractional slot concentrated windings (FSCWs) have been preferably proposed for electric vehicle (EV) applications. Moreover, integrated on-board battery chargers (OBCs), which employ the powertrain elements in the charging process, promote the zero-emission future envisaged for transportation through the transition to EVs. Based on the available literature, the employed machine, as well as the adopted winding configuration, highly affects the performance of the integrated OBC. However, the optimal design of the FSCW-based PM machine in the charging mode of operation has not been conceived thus far. In this paper, the design and multi-objective optimization of an asymmetrical 12-slot/10-pole integrated OBC based on the efficient magnetic equivalent circuit (MEC) approach are presented, shedding light on machine performance during charging mode. An ‘initial’ surface-mounted PM (SPM) machine is first designed based on the magnetic equivalent circuit (MEC) model. Afterwards, a multi-objective genetic algorithm is utilized to define the optimal machine parameters. Finally, the optimal machine is compared to the ‘initial’ design using finite element (FE) simulations in order to validate the proposed optimization approach and to highlight the performance superiority of the optimal machine over its initial counterpart.
  • The Effect of Hydraulic Fracture Geometry on Well Productivity in Shale Oil Plays with High Pore Pressure

    Arias, Daniela; Klimkowski, Lukasz; Finkbeiner, Thomas; Patzek, Tadeusz (Energies, MDPI AG, 2021-11-18) [Article]
    We propose three idealized hydraulic fracture geometries (“fracture scenarios”) likely to occur in shale oil reservoirs characterized by high pore pressure and low differential in situ stresses. We integrate these geometries into a commercial reservoir simulator (CMG-IMEX) and examine their effect on reservoir fluids production. Our first, reference fracture scenario includes only vertical, planar hydraulic fractures. The second scenario has stimulated vertical natural fractures oriented perpendicularly to the vertical hydraulic fractures. The third fracture scenario has stimulated horizontal bedding planes intersecting the vertical hydraulic fractures. This last scenario may occur in mudrock plays characterized by high pore pressure and transitional strike-slip to reverse faulting stress regimes. We demonstrate that the vertical and planar fractures are an oversimplification of the hydraulic fracture geometry in anisotropic shale plays. They fail to represent the stimulated volume geometric complexity in the reservoir simulations and may confuse hydrocarbon production forecast. We also show that stimulating mechanically weak bedding planes harms hydrocarbon production, while stimulated natural fractures may enhance initial production. Our findings reveal that stimulated horizontal bedding planes might decrease the cumulative hydrocarbon production by as much as 20%, and the initial hydrocarbon production by about 50% compared with the reference scenario. We present unique reservoir simulations that enable practical assessment of the impact of varied hydraulic fracture configurations on hydrocarbon production and highlight the importance of constraining present-day in situ stress state and pore pressure conditions to obtain a realistic hydrocarbon production forecast.
  • The creation of calcite microcrystals and microporosity through deep burial basinal flow processes driven by plate margin obduction – A realistic model?

    Wei, Wenwen; Whitaker, Fiona; Hoteit, Hussein; Vahrenkamp, Volker (Marine and Petroleum Geology, Elsevier BV, 2021-11-15) [Article]
    Calcite microcrystals and associated microporosity are ubiquitous and extensively developed in Jurassic and Cretaceous carbonate sequences in the Middle East. Clumped isotope analyses of calcite microcrystals in the Lower Cretaceous Thamama-B strata in UAE reservoirs indicate temperatures of 60–90 °C and burial of 1.5–2.5 km suggesting formation synchronous with and updip of Late Cretaceous ophiolite obduction at the Eastern Arabian continental margin. Assuming that recrystallization of precursor calcite to calcite microcrystals requires initially undersaturation to drive dissolution/re-precipitation a basin-scale 2D reactive transport model (RTM) was constructed. The model is constrained by hydro-mechanical simulations and used to quantitatively evaluate the hypothesis that the formation of calcite microcrystals and associated microporosity is driven by expulsion of compaction fluids during rapid burial. The combined influence of fluid flux and cooling results in trace net calcite dissolution (porosity increase <0.1 vol %) focused at depths of 2.7–5.1 km. The presence of even minor amounts of minerals with common ions (dolomite and anhydrite) induces additional dissolution but does not change its’ spatial distribution. Whilst RTMs only yield a minimum estimate of the degree of recrystallization that likely occurs driven by calcite disequilibrium, simulations suggest these reactions occur at temperatures of 95–170 °C, markedly higher than those estimated from clumped isotopes of the calcite microcrystals. Mixing of fluids leaked from underlying strata up faults into the lateral flow system could play an important role in burial diagenesis, with the slow leakage dissolving up to ten times greater mass of calcite than a shorter pulse of equivalent fluid volume. A previously unrecognized effect of the introduction of H2S(aq)-rich fluids, derived from accelerated thermal maturation or thermochemical sulphate reduction (TSR), into formation water with high CO2(aq) concentration is CO2 degassing that drives net calcite precipitation (<3 vol %). This suite of numerical simulations suggest that calcite disequilibrium will have occurred in fluids expelled during ophiolite obduction, but that any associated recrystallization will have occurred at depths greater than those inferred from temperatures measured in the calcite microcrystals. Recrystallization at shallower depths may have been associated by fluid-mixing around the injection points, but reaction kinetics suggests that laterally pervasive alteration would require very rapid flow rates. These results suggest that alternative mechanism(s) are needed to be considered to explain the extensively developed calcite microcrystals and associated microporosity in Mesozoic carbonates of the Middle East.
  • The potential of self-supervised networks for random noise suppression in seismic data

    Birnie, Claire Emma; Ravasi, Matteo; Liu, Sixiu; Alkhalifah, Tariq Ali (Artificial Intelligence in Geosciences, Elsevier BV, 2021-11-13) [Article]
    Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training. Using blind-spot networks, we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample. Based on the assumption that noise is statistically independent between samples, the network struggles to predict the noise component of the sample due to its randomicity, whilst the signal component is accurately predicted due to its spatio-temporal coherency. Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as post-stack inversion. To conclude our study, the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques: FX-deconvolution and sparsity-promoting inversion by Curvelet transform. By demonstrating that blind-spot networks are an efficient suppressor of random noise, we believe this is just the beginning of utilising self-supervised learning in seismic applications.

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