RSS 1.0Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Recent Submissions
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Analysis and Verification of Islanding Detection Techniques for Grid-integrated PV Systems(IEEE, 2023-03-28) [Conference Paper]The increase in solar energy installation capacity and the versatility of modern power inverters have enabled widespread penetration of distributed generation in modern power systems. Islanding detection techniques allow for fast detection and corrective action in the face of abnormal events. Current standards specify the operational limits for voltage, frequency, and detection time. Grid codes specify the procedures for disconnection to establish safe network maintenance conditions. Passive and active techniques require voltage, current, and frequency measurements and the definition of thresholds for detection. Operational parameters such as load mismatch and quality factors influence the detection capabilities. Falsepositive triggering due to grid transients can lead to unnecessary disconnection of distributed generation resources. In this paper, we analyze the performance of several islanding detection techniques presented in the literature and propose a modified 9-bus benchmark system to verify the robustness of passive and active methods against false-positive detections upon severe gridside transients. Simulation results attest to the superiority of active methods and raise awareness of the susceptibility of all investigated techniques to false islanding detection.
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Physics-Constrained Neural Network (PcNN): Phase Behavior Modeling for Complex Reservoir Fluids(SPE, 2023-03-21) [Conference Paper]The highly nonlinear nature of equation-of-state-based (EOS-based) flash calculations encages high-fidelity compositional simulation, as most of the CPU time is spent on detecting phase stability and calculating equilibrium phase amounts and compositions. With the rapid development of machine learning (ML) techniques, they are growing to substitute classical iterative solvers for speeding up flash calculations. However, conventional data-driven neural networks fail to account for physical constraints, like chemical potential equilibrium (equivalent to fugacity equality in the PT flash formulation) and interphase/intraphase mass conservation. In this work, we propose a physics-constrained neural network (PcNN) that first conserves both fugacity equality and mass balance constraints. To ease the inclusion of fugacity equality, it is reformulated in terms of equilibrium ratios and then introduced with a relaxation parameter such that phase split calculations are extended to the single-phase regime. This makes it technologically feasible to incorporate the fugacity equality constraint into the proposed PcNN model without any computational difficulty. The workflow for the development of the proposed PcNN model includes four steps. Step 1: Perform the constrained Latin hypercube sampling (LHS) to generate representative mixtures covering a variety of fluid types, including wet gas, gas condensate, volatile oil, and black oil. Step 2: Conduct PT flash calculations using the Peng-Robinson (PR) EOS for each fluid mixture. A wide range of reservoir pressures and temperatures are considered, from which we sample the training data for each fluid mixture through grid search. Step 3: Build an optimized PcNN model by including the fugacity equality and mass conservation constraints in the loss function. Bayesian optimization is used to determine the optimal hyperparameters. Step 4: Validate the PcNN model. In this step, we conduct blind validation by comparing it with the iterative PT flash algorithm.
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Parameter Inversion in Geothermal Reservoir Using Markov Chain Monte Carlo and Deep Learning(SPE, 2023-03-21) [Conference Paper]Traditional history-matching process suffers from non-uniqueness solutions, subsurface uncertainties, and high computational cost. This work proposes a robust history-matching workflow utilizing the Bayesian Markov Chain Monte Carlo (MCMC) and Bidirectional Long-Short Term Memory (BiLSTM) network to perform history matching under uncertainties for geothermal resource development efficiently. There are mainly four steps. Step 1: Identifying uncertainty parameters. Step 2: The BiLSTM is built to map the nonlinear relationship between the key uncertainty parameters (e.g., injection rates, reservoir temperature, etc.) and time series outputs (temperature of producer). Bayesian optimization is used to automate the tuning process of the hyper-parameters. Step 3: The Bayesian MCMC is performed to inverse the uncertainty parameters. The BiLSTM is served as the forward model to reduce the computational expense. Step 4: If the errors of the predicted response between the high-fidelity model and Bayesian MCMC are high, we need to revisit the accuracy of the BiLSTM and the prior information on the uncertainty parameters. We demonstrate the proposed method using a 3D fractured geothermal reservoir, where the cold water is injected into a geothermal reservoir, and the energy is extracted by producing hot water in a producer. Results show that the proposed Bayesian MCMC and BiLSTM method can successfully inverse the uncertainty parameters with narrow uncertainties by comparing the inversed parameters and the ground truth. We then compare its superiority with models like PCE, Kriging, and SVR, and our method achieves the highest accuracy. We propose a Bayesian MCMC and BiLSTM-based history matching method for uncertainty parameters inversion and demonstrate its accuracy and robustness compared with other models. This approach provides an efficient and practical history-matching method for geothermal extraction with significant uncertainties.
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Experimental and Computational Fluid Dynamics Investigation of Mechanisms of Enhanced Oil Recovery via Nanoparticle-Surfactant Solutions(Energy & Fuels, American Chemical Society (ACS), 2023-03-21) [Article]The enhancement in surfactant performance at downhole conditions in the presence of nanomaterials has fascinated researchers’ interest regarding the applications of nanoparticle-surfactant (NPS) fluids as novel enhanced oil recovery (EOR) techniques. However, the governing EOR mechanisms of hydrocarbon recovery using NPS solutions are not yet explicit. Pore-scale visualization experiments clarify the dominant EOR mechanisms of fluid displacement and trapped/residual oil mobilization using NPS solutions. In this study, the influence of multiwalled carbon nanotubes (MWCNTs), silicon dioxide (SiO2), and aluminum oxide (Al2O3) nanoparticles on the EOR properties of a conventional surfactant (sodium dodecyl benzene sulfonate, SDBS) was investigated via experimental and computational fluid dynamics (CFD) simulation approaches. Oil recovery was reduced with increased temperatures and micromodel heterogeneity. Adding nanoparticles to SDBS solutions decreases the fingering and channeling effect and increases the recovery factor. The simulation prediction results agreed with the experimental results, which demonstrated that the lowest amount of oil (37.84%) was retained with the micromodel after MWCNT-SDBS flooding. The oil within the micromodel after Al2O3-SDBS and SiO2-SDBS flooding was 58.48 and 43.42%, respectively. At 80 °C, the breakthrough times for MWCNT-SDBS, Al2O3-SDBS, and SiO2-SDBS displacing fluids were predicted as 32.4, 29.3, and 21 h, respectively, whereas the SDBS flooding and water injections at similar situations were at 12.2 and 6.9 h, respectively. The higher oil recovery and breakthrough time with MWCNTs could be attributed to their cylindrical shape, promoting the MWCNT-SDBS orientation at the liquid–liquid and solid–liquid interfaces to reduce the oil–water interfacial tension and contact angles significantly. The study highlights the prevailing EOR mechanisms of NPS.
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Multimode Free-Vibration Decay Column: Small-Strain Stiffness and Attenuation(Journal of Geotechnical and Geoenvironmental Engineering, American Society of Civil Engineers (ASCE), 2023-03-20) [Article]This study presents a simplified resonant column testing method to obtain small-strain dynamic properties of soils in both torsional and flexural vibrations. The method exploits free vibration decay responses of the system produced by manual excitation while the specimen is subjected to an isotropic effective confining stress produced by a vacuum pressure. This method is readily applicable to standard resonant column and torsional shear devices and triaxial cells by attaching a metal bar with one or two accelerometers for manual excitation, but not using an electromagnetic driving plate. This paper describes the apparatus design, test procedure, system calibration, and data analyses, as well as the test results of dynamic properties of a dry sand, including small-strain elastic moduli and damping ratios obtained from the torsional and flexural modes. The results confirm that the suggested method can capture strain-dependent characteristics up to the strains of ∼10−4 beyond typical elastic threshold strains, although the isotropic effective confining stress is limited to ∼90 kPa. This unique testing method provides remarkably consistent and reliable measurement for the dynamic properties of soils, and it avoids any possible bias from the counterelectromotive force.
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The effects of chemical and mechanical interactions on the thermodynamic pressure for mineral solid solutions(Continuum Mechanics and Thermodynamics, Springer Science and Business Media LLC, 2023-03-18) [Article]We use a coupled thermodynamically consistent framework to model reactive chemo-mechanical responses of solid solutions. Specifically, we focus on chemically active solid solutions that are subject to mechanical effects due to heterogeneous stress distributions. The stress generation process is driven solely by volume changes associated with the chemical processes. We use this model to describe the underlying physics during standard geological processes. Furthermore, simulation results of a three-species solid solution provide insights into the phenomena and verify the interleaving between mechanical and chemical responses in the solid. In particular, we show the evolution of the thermodynamic pressure as the system goes to a steady state.
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Long-Term Response of Sand Subjected to Repetitive Simple Shear Loading: Shakedown, Ratcheting, and Terminal Void Ratio(Journal of Geotechnical and Geoenvironmental Engineering, American Society of Civil Engineers (ASCE), 2023-03-16) [Article]Low-amplitude repetitive drained loading may hinder the long-term performance of engineered and natural systems. This study examines the volumetric and shear response of a uniform quarzitic sand subjected to repetitive drained simple shear loading under constant vertical stress while tracking the evolution of the secant stiffness and the small-strain shear modulus. We explore the effects of initial density, initial shear stress and cyclic shear stress amplitude to identify criteria that can be used to anticipate asymptotic volumetric and shear states. We analyze experimental results in reference to the sand response under monotonic simple shear loading. All specimens evolved toward some asymptotic terminal void ratio eT when subjected to simple shear cycles. Contractive specimens exhibited unceasing shear strain accumulation and ratcheting when the normalized shear stress exceeded τ∗=(τo+Δτ)/τult>0.85; on the other hand, dense-dilative specimens exhibited ratcheting only when the normalized shear stress exceeded τ∗=(τo+Δτ)/τult>1.25. The small-strain Gmax and the secant Gpp shear moduli increased during repetitive shear cycles to reflect early fabric changes followed by abrasion/fretting among enduring contacts. Results obtained in this study allow us to propose simple guidelines to predict the asymptotic shear and volumetric response of uniform sands subjected to repetitive simple shear loading for first-order engineering analyses.
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Machine learning in microseismic monitoring(Earth-Science Reviews, Elsevier BV, 2023-03-13) [Article]The confluence of our ability to handle big data, significant increases in instrumentation density and quality, and rapid advances in machine learning (ML) algorithms have placed Earth Sciences at the threshold of dramatic progress. ML techniques have been attracting increased attention within the seismic community, and, in particular, in microseismic monitoring where they are now being considered a game-changer due to their real-time processing potential. In our review of the recent developments in microseismic monitoring and characterisation, we find a strong trend in utilising ML methods for enhancing the passive seismic data quality, detecting microseismic events, and locating their hypocenters. Moreover, they are being adopted for advanced event characterisation of induced seismicity, such as source mechanism determination, cluster analysis and forecasting, as well as seismic velocity inversion. These advancements, based on ML, include by-products often ignored in classical methods, like uncertainty analysis and data statistics. In our assessment of future trends in ML utilisation, we also see a strong push toward its application on distributed acoustic sensing (DAS) data and real-time monitoring to handle the large amount of data acquired in these cases.
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Data-Driven-Based Vector Space Decomposition Modeling of Multiphase Induction Machines(IEEE Transactions on Energy Conversion, Institute of Electrical and Electronics Engineers (IEEE), 2023-03-13) [Article]For contemporary variable-speed electric drives, the accuracy of the machine's mathematical model is critical for optimal control performance. Basically, phase variables of multiphase machines are preferably decomposed into multiple orthogonal subspaces based on vector space decomposition (VSD). In the available literature, identifying the correlation between states governed by the dynamic equations and the parameter estimate of different subspaces of multiphase IM remains scarce, especially under unbalanced conditions, where the effect of secondary subspaces sounds influential. Most available literature has relied on simple RL circuit representation to model these secondary subspaces. To this end, this paper presents an effective data-driven-based space harmonic model for n-phase IMs using sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover the IM governing equations. Moreover, the proposed approach is computationally efficient, and it precisely identifies both the electrical and mechanical dynamics of all subspaces of an IM using a single transient startup run. Additionally, the derived model can be reformulated into the standard canonical form of the induction machine model to easily extract the parameters of all subspaces based on online measurements. Eventually, the proposed modeling approach is experimentally validated using a 1.5 Hp asymmetrical six-phase induction machine.
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Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir.(Scientific reports, Springer Science and Business Media LLC, 2023-03-09) [Article]Carbonate rocks present a complicated pore system owing to the existence of intra-particle and interparticle porosities. Therefore, characterization of carbonate rocks using petrophysical data is a challenging task. Conventional neutron, sonic, and neutron-density porosities are proven to be less accurate as compared to the NMR porosity. This study aims to predict the NMR porosity by implementing three different machine learning (ML) algorithms using conventional well logs including neutron-porosity, sonic, resistivity, gamma ray, and photoelectric factor. Data, comprising 3500 data points, was acquired from a vast carbonate petroleum reservoir in the Middle East. The input parameters were selected based on their relative importance with respect to output parameter. Three ML techniques such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and functional network (FN) were implemented for the development of prediction models. The model's accuracy was evaluated by correlation coefficient (R), root mean square error (RMSE), and average absolute percentage error (AAPE). The results demonstrated that all three prediction models are reliable and consistent exhibiting low errors and high 'R' values for both training and testing prediction when related to actual dataset. However, the performance of ANN model was better as compared to other two studied ML techniques based on minimum AAPE and RMSE errors (5.12 and 0.39) and highest R (0.95) for testing and validation outcome. The AAPE and RMSE for the testing and validation results were found to be 5.38 and 0.41 for ANFIS and 6.06 and 0.48 for FN model, respectively. The ANFIS and FN models exhibited 'R' 0.937 and 0.942, for testing and validation dataset, respectively. Based on testing and validation results, ANFIS and FN models have been ranked second and third after ANN. Further, optimized ANN and FN models were used to extract explicit correlations to compute the NMR porosity. Hence, this study reveals the successful applications of ML techniques for the accurate prediction of NMR porosity.
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Data-Driven Machine Learning Modeling of Mineral/CO2/Brine Wettability Prediction: Implications for CO2 Geo-Storage(SPE, 2023-03-07) [Conference Paper]CO2 wettability and the reservoir rock-fluid interfacial interactions are crucial parameters for successful CO2 geological sequestration. This study implemented the feed-forward neural network to model the wettability behavior in a ternary system of rock minerals (quartz and mica), CO2, and brine under different operating conditions. To gain higher accuracy of the machine learning models, a sufficient dataset was utilized that was recorded by conducting a large number of laboratory experiments under a realistic pressure range, 0 – 25 MPa and the temperatures range, 298 – 343 K. The mica substrates were used as a proxy for the caprock, and quartz substrates were used a proxy for the reservoir rock. Different graphical exploratory data analysis techniques, such as heatmaps, violin plots, and pairplots were used to analyze the experimental dataset. To improve the generalization capabilities of the machine learning models k-fold cross-validation method, and grid search optimization approaches were implemented. The machine learning models were trained to predict the receding and advancing contact angles of mineral/CO2/brine systems. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented ML model accurately predicted the wettability behavior under various operating conditions. The training and testing average absolute percent relative errors (AAPE) and R2 of the FFNN model for mica and quartz were 0.981 and 0.972, respectively. The results confirm the accuracy performance of the ML algorithms. Finally, the investigation of feature importance indicated that pressure had the utmost influence on the contact angles of the minerals/CO2/brine system. The geological conditions profoundly affect rock minerals wetting characteristics, thus CO2 geo-storage capacities. The literature severely lacks advanced information and new methods for characterizing the wettability of mineral/CO2/brine systems at geo-storage conditions. Thus, the ML model's outcomes can be beneficial for precisely predicting the CO2 geo-storage capacities and containment security for the feasibility of large-scale geo-sequestration projects.
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An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach(SPE, 2023-03-07) [Conference Paper]Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes and storage potential at each storage site. To better understand the integrity of the caprock after injecting CO2, it is necessary to develop robust and fast tools to evaluate the safe CO2 injection duration. This study applied deep learning (DL) techniques, such as fully connected neural networks, to predict the safe injection duration. A physics-based numerical reservoir simulator was used to simulate the movement of CO2 for 170 years following a 30-year CO2 injection period into a deep saline aquifer. The uncertainty variables were utilized, including petrophysical properties such as porosity and permeability, reservoir physical parameters such as temperature, salinity, thickness, and operational decision parameters such as injection rate and perforation depth. As mentioned earlier, the reservoir model was sampled using the Latin-Hypercube sampling approach to account for a wide range of parameters. Seven hundred twenty-two reservoir simulations were performed to create training, testing, and validation datasets. The DNN model was trained, and several executions were performed to arrive at the best model. After multiple realizations and function evaluations, the predicted results revealed that the three-layer FCNN model with thirty neurons in each layer could predict the safe injection duration of CO2 into deep saline formations. The DNN model showed an excellent prediction efficiency with the highest coefficient of determination factor of above 0.98 and AAPE of less than 1%. Also, the trained predictive models showed excellent agreement between the simulated ground truth and predicted trapping index, yet 300 times more computationally efficient than the latter. These findings indicate that the DNN-based model can support the numerical simulation as an alternative to a robust predictive tool for estimating the performance of CO2 in the subsurface and help monitor the storage potential at each part of the GCS project.
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Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach(SPE, 2023-03-07) [Conference Paper]Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are commonly located worldwide and have the potential to be good sources of long-term storage of carbon dioxide (CO2). The numerical reservoir simulation models are an excellent source for evaluating the likelihood and comprehending the physics underlying behind the interaction of CO2 and brine in subsurface formations. For various reasons, including the rock's highly fractured and heterogeneous nature, the rapid spread of the CO2 plume in the fractured network, and the high capillary contrast between matrix and fractures, simulating fluid flow behavior in NFR reservoirs during CO2 injection is computationally expensive and cumbersome. This paper presents a deep-learning approach to capture the spatial and temporal dynamics of CO2 saturation plumes during the injection and monitoring periods of Geological Carbon Sequestration (GCS) sequestration in NFRs. To achieve our purpose, we have first built a base case physics-based numerical simulation model to simulate the process of CO2 injection in naturally fractured deep saline aquifers. A standalone package was coded to couple the discrete fracture network in a fully compositional numerical simulation model. Then the base case reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and decision parameters. These samples generated a massive physics-informed database of around 900 cases that provides a sufficient training dataset for the DL model. The performance of the DL model was improved by applying multiple filters, including the Median, Sato, Hessian, Sobel, and Meijering filters. The average absolute percentage error (AAPE), root mean square error (RMSE), Structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), and coefficient of determination (R2) were used as error metrics to examine the performance of the surrogate DL models. The developed workflow showed superior performance by giving AAPE less than 5% and R2 more than 0.94 between ground truth and predicted values. The proposed DL-based surrogate model can be used as a quick assessment tool to evaluate the long-term feasibility of CO2 movement in a fracture carbonate medium.
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Improved Amott Tests Help Quantify Primary Driving Forces in Spontaneous Imbibition in Water-Wet and Oil-Wet Limestone Rock(SPE, 2023-03-07) [Conference Paper]The improved oil recovery techniques, such as customized ionic composition waterflood or "smart-water" flood, are being developed to increment crude oil production. Counter-current spontaneous imbibition of brine into oil-saturated rock is a critical mechanism of recovery of the crude oil bypassed in highly-heterogeneous carbonate rocks. In laboratory, spontaneous imbibition in the Amott cell experiment is the main instrument to explore oil recovery from oil-saturated core plugs at different wettability conditions. The classical Amott test, however, masks a number of flaws that hinder interpretation of the physical phenomena in recovery dynamics and precise modeling of the cumulative recovery profiles. In this work, we identify these flaws in the spontaneous imbibition experiments with mixed-wet limestone samples saturated with crude oil. We describe an improved Amott method and study crude oil recovery from mixed-wet carbonate core plugs. The introduced modifications of the Amott test ensure reliable and reproducible results for both non-wetting mineral and crude oils. Finally, we show that the resulted smooth recovery profiles of oil production can be described with a mathematical model with high accuracy. For the first time, we show that generalized extreme value (GEV) distribution can be applied to model cumulative oil production from mixed-wet carbonate core samples.
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Fully Coupled Hydromechanical Approach for Flow in Fractured Rocks Using Darcy-Brinkman-Biot(SPE, 2023-03-07) [Conference Paper]Coupling flow with geomechanical processes at the pore scale in fractured rocks is essential in understanding the macroscopic fluid flow processes of interest, such as geothermal energy extraction, CO2 sequestration, and hydrocarbon production from naturally and hydraulically fractured reservoirs. To investigate the microscopic (pore-scale) phenomena, we present a fully coupled mathematical formulation of fluid flow and geomechanical deformation to model the fluid flow in fractured rocks. In this work, we employ a Darcy-Brinkman-Biot approach to describe the fully coupled flow and geomechanical processes in fractured rocks at the pore scale. Darcy-Brinkman-Stokes (DBS) model is used to model multi-scale flow in the fractured rocks, in which fracture flow is described by Navier-Stokes equations and flow in the surrounding matrix is modeled by Darcy's law. With this approach, a unified conservation equation for flow in both media (fracture and matrix) is applied. We then apply Biot's poroelasticity theory and Terzaghi's effective stress theory to capture the geomechanical deformation. The continuity of the fluid pressure is imposed to connect the DBS equation and the stress-seepage equation. This coupled model is employed to determine the permeability within the microfracture. Numerical results show that this coupled approach can capture the permeability under the effects of solid deformation and multi-scale formation. We develop a fully coupled model to capture the pore-scale flow-geomechanically process in fractured rocks. To our knowledge, the fully coupled framework is developed and applied to characterize fracture permeability at the pore scale in fractured rocks for the first time.
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Drilling Monitoring System: Mud Motor Condition and Performance Evaluation(SPE, 2023-03-07) [Conference Paper]Condition monitoring of bottom hole assembly (BHA) is essential during the different lifecycle stages of the drilling process, whether during the planning, implementation, or post-job failure analysis. Mud motor condition evaluation can assist in preventing mud motor damage and increasing drilling efficiency. This paper aims to develop a monitoring system that combines field data, data analytics and physics-based modelling to evaluate mud motor condition and performance. The drilling monitoring system is a set of modelling and analysis tools that utilize actual drilling data, power section performance data and drillstring design to recreate the drilling process. An unprocessed drilling dataset is required to assess the drilling operations (rotating or sliding mode, rotating off-bottom, backreaming, connections) and reconstruct the borehole trajectory from the measured survey and duty cycle (rotating and sliding mode). Interaction of the BHA and the borehole generate side forces and bending moments along the length of the BHA that are evaluated at each depth increment during the drilling process. Generated power and efficiency of the mud motor are calculated and incorporated into the dynamic simulation. The case study investigates two motor runs in vertical and inclined sections. Dynamic modelling and extensive data analytics assist in visualizing and correlating the input and output variables during the drilling process. The continuous evaluation of the differential pressure on the motor is the primary parameter that is investigated. The motor condition is established with a continuous wear-off test while drilling and correlation matrices to indicate a constant motor state for 135 hours. The system accurately monitors the motor's operating efficiency, with the additional advantage that the mud motor and drill bit performance are differentiated. Precise adjustments of the drilling parameters for the optimum depth of cut positively impact motor efficiency. In addition, an interesting observation shows that accurate modelling of downhole and surface torque provides significant insights regarding bit state. The results demonstrate that with the current methodology decrease in drilling efficiency is detected and is associated with bit wear. The work enables the evaluation of the mud motor condition and performance by utilizing a monitoring system and actual drilling data. The drilling monitoring system is a modelling analysis tool that can provide continuous feedback to the drilling operators about the condition of the BHA. Hence, it enables a real-time optimization process to manage mud motor condition and enhance drilling efficiency.
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Advanced Hole Cleaning in Horizontal Wells: Experimental Investigation Supported by a Downhole Clamp-On Tool(SPE, 2023-03-07) [Conference Paper]Up to 80% of stuck pipe events are hole cleaning related in the case of high-angle wells. Therefore, significant attention should be given to understanding hole cleaning as it is crucial to restricting stuck pipe-related non-productive time (NPT). In order to optimize hole cleaning efficiency, the fundamental objective of the proposed paper is to experimentally investigate cuttings transport supported by a downhole clamp-on tool. This work approaches existing challenges by designing and building a custom flow loop that recreates the drilling environment of horizontal wells. The study provides additional steps and new ideas in developing a reliable experimental setup for a proper hole cleaning investigation. Accordingly, the process includes comprehensive dimensional analysis, detailed design, and building a desired experimental flow loop setup. A unique mechanical design allows pipe rotation while achieving a closed-loop system. A clamp-on tool assists in agitating the cuttings to reduce accumulation at the bottom of the borehole. Experimental performance with various cuttings compares scenarios with and without pipe rotation. Among the key factors influencing cuttings transport in horizontal wells are drill pipe rotation (RPM), flow rate (Q), mud rheology, cuttings size, flow regime, and penetration rate (ROP). This research focuses on the mechanical removal of solid cuttings. Experimental work emphasizes cuttings' behavior showing different patterns for their movement in deviated wells by utilizing image processing. Drillstring rotation proves to be a crucial factor for efficient hole cleaning. The specific shape and dimensions of the clamp- on tool affect the efficiency of the hole cleaning process and impact the distance covered by the agitated cuttings downstream of the tool. The concept of the tool depends on blades that agitate cuttings as it rotates. Optimum tool design considers the physical properties of the fluid and the cuttings. The results show that as the tool agitates cuttings and moves them into the higher velocity region, the cuttings advance with the flow, which improves cuttings transport and reduces bedding formation. Assuming low flow rates, tool application increased average particle velocity within the tool more than four times (372%) and twice after the tool. In addition, differential pressure (Δp) shows a significant decrease while the tool operates, indicating improvement in hole cleaning. Lab-scaled flow loop development aims to simulate drilling conditions with drillpipe rotation and different downhole clamp-on tool geometries. The results show different flow patterns from experimental observations of liquid-particles flow in the horizontal wellbore, assisted by the proposed downhole clamp- on tool. The innovative tool design is a promising step in reducing hole cleaning issues with mechanical- assisted tools.
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Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning(SPE, 2023-03-07) [Conference Paper]Geologic CO2 sequestration (GCS) is a promising engineering measure to reduce global greenhouse emissions. However, accurate detection of CO2 leakage locations from underground traps remains a challenging problem. This study proposes a workflow that combines Bayesian inversion and deep learning algorithms to detect the sites of CO2 leakage. There are four main steps in the workflow. Step 1: we identify the key uncertainty parameters. Here we mean the CO2 leakage location. Then we get the training set using Latin Hypercube Sampling (LHS) method and perform the high-fidelity simulation using CMG. Step 2: we train the surrogate model using the data set collected from the last step, in which the Bayesian optimization is used to tune the hyperparameters automatically. Step 3: we perform the Bayesian inversion to invert the CO2 leakage location, in which the surrogate serves as the forward model to reduce the computational expense. Step 4: we feed the inverted CO2 leakage location into the high-fidelity model to produce the pressure response. If the error between the pressure response between the surrogate and the high-fidelity model is small enough, the solution is accepted. Otherwise, the accuracy of the surrogate model and the convergence of the Bayesian inversion process are revisited. We validate this method using a synthetic model of CO2 injection. Results show that the proposed Bayesian inversion assisted by the deep learning algorithm can accurately detect the CO2 leakage location with narrow uncertainties. This approach provides an accurate and efficient way to detect CO2 leakage locations in real-time applications.
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Biostratigraphy of the Numidian Formation and its underlying Tellian unit, based on calcareous nannofossils in Northern Tunisia: Implication on their stratigraphic vs. tectonic relationships(Journal of African Earth Sciences, Elsevier BV, 2023-03-02) [Article]A detailed biostratigraphic study, based on calcareous nannofossils, was performed to re-evaluate the stratigraphic interpretation of the Numidian Formation and its structural relationship with the underlying El Haria, Boudabbous and Souar formations Tellian units (i.e., stratigraphic vs. tectonic). The new biostratigraphic results indicate that the Numidian Formation is not restricted to a Miocene age as considered in some recent works but spans from Oligocene (Rupelian and Chattian, NP23 to NP25) to early Miocene (Aquitanian and Burdigalian, NN1 to NN3). The Cap Serrat succession can be retained as the type section for the Tunisian Numidian Formation, where both the Chattian-Aquitanian (Oligocene/Miocene boundary) and the Aquitanian-Burdigalian transitions have been characterized. Based on the new calcareous nannofossil findings, several duplications of the sedimentary succession were identified. They include the thick shales and marls of El Haria (Maastrichtian-Paleocene) and Souar (Eocene) formations, thrusted by the Numidian Formation. The updated regional structural cross-sections, confirm that the Numidian Formation is currently juxtaposed by southward major thrust contact to the underlying Kasseb structural unit in the studied area. The latter is intensively affected by poly-phased tectonics, resulting in the thrust events associated with the Alpine phase inversion. This has to be taken into consideration in oil and gas exploration in the area of the fold-thrust belt system, in both onshore and offshore Northern Tunisia.
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Deep Learning Solves Complex Physics With an Example of CO2 Mineralization(2023-03) [Conference Paper]
