Leveraging Artificial Intelligence and Geomechanical Data for Accurate Shear Stress Prediction in CO2 Sequestration within Saline Aquifers (Smart Proxy Modeling)

This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised learning, to construct Smart Proxy Models, which are critical to ensuring the safety and effectiveness of Carbon Capture and Storage operations. By training the Smart Proxy Model with reservoir simulations that incorporate varying geological properties and geomechanical data, we will be able to predict the distribution of shear stress. The ability to accurately predict shear stress is crucial to mitigating the potential risks associated with Carbon Capture and Storage operations. The development of a geomechanical Smart Proxy Model will enable more efficient and reliable subsurface modeling decisions in Carbon Capture and Storage operations, ultimately contributing to the safe and effective storage of CO2 and the global effort to combat climate change

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry.

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached by following Boolean logic. However, human thinking is based on a more complex logic that includes the ability to process uncertainty. In value of information assessment, it is often desirable to make decisions based on multiple economic criteria which, if independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims at enriching the value of information methodology by integrating fuzzy logic into the decision-making process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors’ knowledge, this is the first use of a fuzzy inference system in the specified knowledge domain. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation. Sensitivity analysis is undertaken for several membership functions used in the case study to assess the impact that shifting, narrowing and stretching the membership relationship has on the value of information. The results of the sensitivity study show that, depending on the shifting, the membership functions lead to different decisions; additional sensitivities to the type of membership functions are investigated, including the functions’ parameters

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 "out of leverage" data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements

The Impact Of Stress Dependent Permeability Alteration On Gas Based EOR In The Bakken Formation

Effective stress exerted on porous rocks can change and alter reservoir permeability accordingly during reservoir development. The permeability evolution under different reservoir statues will impact oil production and EOR design in the Bakken shale porous media. An accurate permeability model can improve capturing the fluid transport mechanism and create a reliable long-term dynamic fluid forecast via reservoir simulation. This research is focused on studying permeability alteration behavior under different pressure circumstances. The reservoir gradually loses its original pore pressure during production, increasing reservoir net effective stress. Therefore, a reduction in reservoir properties such as permeability or porosity can occur in response to net stress change within the pores due to the withdrawal of the fluids from the reservoir. In contrast, a fluid injection can reduce formation pressure drop and maintain pressure during the development process in tight rock reservoirs. However, physical parameters (e.g., permeability) cannot be fully recovered, and back to its initial value, this nature of rock is characterized as stress sensitivity or hysteresis. Stress-dependent properties are hard to model accurately in reservoir simulation because of the uncertainty associated with the stress-dependent coefficients and correlations. The conventional reservoir simulators use the compressibility concept to consider the change of pore volume, where the rock properties are usually assumed to be insensitive to the evolution of the stress state. However, reservoir compaction and stress changes can significantly impact reservoir management and production performance. In this study, a review of different rock characterizations of the Three forks and Bakken core samples to determine stress dependency of permeability and its hysteresis during pressurizing/ depressurizing rock samples is conducted. Core samples from the Middle Bakken formation in North Dakota for further permeability alteration experiments are utilized. This data will be used to evaluate the permeability behavior with respect to critical pressure known as pressure shock. Also, the data analytic approach to model permeability on a larger scale based on several inputs such as depth, different net confining stress, and porosity is performed. Numerical reservoir simulation using Bakken and Three Forks formation is utilized to integrate permeability pressure correlation in simulation modeling and compare several injection scenarios with non-sensitive permeability models. The results indicate that ignoring the effect of slope discontinuity at a critical effective stress using the same equation for a whole range of data is inaccurate. Indeed, developing permeability-stress correlations cause inapplicable mathematical models and, consequently, erroneous permeability damage prediction. Following this concept, modifying the correlation for two Bakken cores shows that considering the critical points on each hysteresis path could improve the final form of the stress-dependent permeability relationship. Also, machine learning modeling using available lab core data can be used as an alternative method to capture Bakken and Three Forks permeability changes under different net confining stress while incorporating the critical pressure effect. Furthermore, to evaluate the several gas injection scenarios, the timely reservoir pressure change is divided into three distinct regions where critical effective pressure impact and miscibility of gas injection vary based on current reservoir statutes. The results demonstrate that gas injection in these formations is a strong function of fracture/matrix permeability damage. Compared to the model without considering stress-dependent permeability, the cumulative production could reduce because the permeability decreases along with reservoir pressure decline. As a result, considering permeability modeling in numerical simulation can help to understand the role of different injection scenarios and enhance the knowledge for controlling and managing reservoir production by proper operation decisions in unconventional reservoirs

Flow Simulation and Characterization of Fracture Systems Using Fast Marching Method and Novel Diagnostic Plots

Recently, the industrial trend of hydraulic fracturing is reducing the cluster spacing while increasing the fluid and proppant usage, which often generates complex fracture networks. The challenge from this trend is to understand and characterize the complex fracture networks. Recently, a novel approach has been proposed based on the high-frequency asymptotic solution of the diffusivity equation leading to the Eikonal equation. The Eikonal equation governs the pressure front propagation and can be solved by a front-tracking algorithm called Fast Marching Method (FMM). In this dissertation, we extend this method to complex fracture networks characterization and simulation, using novel diagnostic plots and FMM-based simulation. First, we develop novel diagnostic plots for complex fracture networks characterization. We directly use the field data to calculate the well drainage volume, instantaneous recovery ratio (IRR) and w(τ) function. The w(τ) function serves as a diagnostic plot to detect fracture geometry and flow regimes and the IRR plot is used to detect fracture conductivity. Second, we extend the FMM-based simulation workflow to local grid refinements (LGRs). The detailed workflow is proposed to generate the computational grid for the diffusive time of flight (DTOF) calculation. We use various models to validate the accuracy and computational efficiency of this workflow. In addition, we investigate various discretization schemes for the transition between local and global domain. Third, we extend the FMM-based simulation workflow to embedded discrete fracture model (EDFM). We utilize a novel gridding to link the embedded discrete fractures and the matrix based on Delaunay triangulation. Using the DTOF as a spatial coordinate, the FMM-based flow simulation reduces the 3D complex fracture networks simulation to an equivalent 1D simulation. Multiple examples are shown to validate the accuracy and computational efficiency of this workflow. Lastly, we investigate the impact of tighter cluster spacing of the hydraulic fractures using the Eagle Ford field data. The hydraulic fracture propagation simulator Mangrove® is used to generate the fracture patterns based on the completion data. A manual history matching is conducted to match the field injection treatment pressure. The impact of cluster spacing is examined through the calibrated fracture models

Coupled geomechanics and transient multiphase flow at fracture-matrix interface in tight reservoirs.

Fractured hydrocarbon reservoirs play a significant role in the world economy and energy markets. Fluid injection (normally water) forces the hydrocarbons out of the reservoirs. Geomechanics, externally applied stress on the rock, play a significant role in the oil recovery from fractured reservoirs. Subsurface fluid injection modifies pore pressure and in-situ stresses locally. In response to the pressure/stress combined effects, the pores and fracture regions undergo deformation. Similarly, it is a well-known fact that pore volume significantly impacts the absolute and relative permeability of fractured tight reservoirs. The governing factors that characterize multiphase fluid flow mechanisms in naturally-fractured tight reservoirs - such as wellbore stability, CO2 sequestration and improved hydrocarbon recovery - are relative permeability and capillary pressure. Although the effects of geomechanical parameters on single-phase fluid flow in naturally-fractured tight reservoirs are well documented, the interdependence between geomechanical and multiphase flows are severely lacking. This study aims to bridge this knowledge gap using advanced numerical techniques, focusing on accurately capturing complex flow phenomena at the fracture-matrix interface to enhance the accuracy of predicting oil recovery from naturally-fractured tight reservoirs, leading towards more efficient operations and reduced costs. Extensive sets of numerical investigations have been carried out in the present study, using an advanced Computational Fluid Dynamics (CFD) solver, to accurately capture transient multiphase flow (oil and water) phenomena within naturally-fractured tight reservoirs. Special attention has been paid towards accurate multiphase flow modelling and characterisation at the fracture-matrix interface. The numerical models have been validated against Berea Sandstone experimental data. Two separate numerical models have been developed with the aim to identify the most appropriate modelling technique for accurate numerical predictions of multiphase flow in naturally-fractured tight reservoirs. These two models are based on duct flow theory and flow through porous medium theory, respectively, while the Brooks and Corey method has been utilised to compute fluid saturation, relative permeability and capillary pressure at the fracture-matrix interface. The results obtained show that the difference between the numerical and experimental results is 30% when duct flow model is considered, while it is 2.57% when porous medium is considered. In order to critically evaluate the dependence of multiphase flow on the geomechanical parameters of naturally-fractured tight reservoirs, a one-way FEACFD coupling scheme has been implemented in the present study, not taking into consideration the pore pressure. The effects of externally applied stress loading on the geomechanical (porosity and fracture aperture) and multiphase flow characteristics (permeability, capillary pressure, relative permeability and fluid saturation) at the fracture-matrix interface have been thoroughly analysed. For accurate modelling and numerical predictions in naturally-fractured tight reservoirs, a viscous loss term has been incorporated in the momentum-conservation equations. The numerical predictions from the one-way coupled model matches well with Clashach core flooding experimental data, with 9% average difference between the two. The results obtained clearly indicate that external stress loading has significant impact on the geomechanical and multiphase flow characteristics at the fracture-matrix interface. Finally, a novel numerical model has been developed based on the full coupling scheme, with the aim to enhance the accuracy of the numerical predictions regarding oil recovery from naturally-fractured tight reservoirs for efficient and cost effective operations. The porous elasticity interface is coupled with multiphase flow in porous media where the mass conservation of each phase, and an extended Darcy's equation, underpin multiphase flow characteristics. The fully coupled model takes into consideration the pore pressure and has been validated against Clashach core flooding experimental data. The developed model has been shown to significantly enhance the prediction accuracy from 9%, for one-way coupled model, to 4%, and has the ability to capture complex multiphase flow phenomena at the fracture-matrix interface. Moreover, the novel model accurately predicts the effects of geomechanical parameters on multiphase flow characteristics. It is envisaged that the novel fully coupled model developed in this study will pave the way for future scientific research in the area of geomechanical-fluid flow coupling for enhanced oil recovery in naturally-fractured tight reservoirs

DEVELOPMENT OF EFFICIENTLY COUPLED FLUID FLOW AND GEOMECHANICS MODEL FOR HIGHLY FRACTURED RESERVOIRS

Number of unconventional developments have increased greatly in the recent years to meet the global demand on hydrocarbon usages. Completion work can be very challenging due to complex characteristic of unconventional reservoir, which directly affects production performance. A rapid decline in parent well production has recently been observed in many unconventional developments, which subsequently increases the number of infill wells. Hydraulic fractures created from infill wells tend to propagate towards the parent well as a result of reservoir depletion. The interference between parent and infill well fractures due to a tight spacing is the main cause of poor production performance in both parent and infill wells. Stress change can be observed as the reservoir depletes due to the poroelastic effect. This leads to complex fracture geometry created during infill well completion, which is difficult to predict and usually causes negative impact on well production. Therefore, it is important to be able to predict depletion-induced stress change in the reservoirs with complex fracture geometries. The prediction of fracture interference is sometimes not accurate compared to the field observation as most studies mainly focus on stress evolution in planar fracture geometries since it is difficult to model complex fracture geometries. Unstructured grids have been implemented to handle such problems, but it usually comes with high computational cost and less computational efficiency, which is not a good option when simulating a field-scaled reservoir. This has become the main motivation of this work, which is to develop a coupled geomechanics and fluid flow model to characterize stress evolution due to reservoir depletion in highly fractured reservoirs with high computational efficiency. In this dissertation, I have developed a coupled geomechanics and fluid flow using a well-known sequentially coupled method called fixed stress-split to capture stress change in both magnitude and orientation during reservoir depletion. The coupling method was selected to ensure stability of the simulation while maintaining low computational cost. Embedded Discrete Fracture Model (EDFM) was coupled with the model to gain capability in simulating complex fracture geometries using structured gridding system. This significantly improves computational efficiency as well as opens the possibility of exploring cases with complex fracture network. The simulator was developed based on an open-source code called Open source Field Operation And Maniputation (OpenFOAM), allowing the simulation to be conducted in full 3D without significantly impacting computational cost. The developed model was used to predict refracturing performance in a highly fractured reservoir as well as infill well completion in a multi-payzone reservoir. In addition, the model was coupled with complex fracture propagation model to study how heterogeneous stress state affects fracture geometry created during infill well treatment, which can greatly help predict fracture interaction and maintain production performance. Two-phase flow was also implemented to the model for some field case studies such as water injection. The results observed in this study suggest that fracture geometry is a main factor that affects stress change in magnitude and orientation. The presence of natural fractures and fracture spacing plays an important role in refracturing performance in highly fractured reservoirs. Critical time can be used to determine when the refracturing should be performed to ensure the successful results and obtain optimum refracturing locations. For the infill well completion in a reservoir with multiple pay zones, it is suggested that both parent wells should be placed in different layers to mitigate stress change in the infill zone. Fracture penetration effect should also be considered as it accelerates stress reorientation in the infill zone. Severe asymmetrical fracture geometries with the longer side being closer to the depleted zone can be observed in the infill well with short spacing when coupling fracture propagation model with the reservoir-geomechanics model. These results are crucial and can be a guideline for field operation in reservoirs with complex fracture network

Geomechanical characterizations and correlations to reduce uncertainties of carbonate reservoir analysis

Although carbonate reservoirs hold a wealth of hydrocarbon, they are among the most difficult types of reservoirs to be characterized. Carbonate reservoirs by nature have complex depositional environments and diagenetic processes in which brittle, ductile, fractured rocks, and vugular pores may all exist within small interval. This huge variance in the rock mechanical properties can cause challenges in the reservoir\u27s development, especially in applications related to geomechanics. The main objective of this research is to geomechanically characterize and correlate the carbonate mechanical properties with their petrophysical properties. A comprehensive review for the geomechanical-petrophysical properties of carbonates was conducted from previous studies. Data from offset well have also been used to develop an integrated methodology that examines the uncertainty of carbonate wellbore integrity. The results present a new engineering classification to evaluate the carbonate drillability and deformability. Additional developments regarding the relationships between the carbonate compressive strength and confining pressure, maximum shear stress and mean stress, and internal friction angle and unconfined compressive strength (UCS) are systematically investigated based on the compiled database. New correlations to predict the UCS and Young\u27s modulus of each carbonate type have been developed from the petrophysical properties. Applying P90 as a threshold on the estimated minimum mud weight proved to be conservative. For fracture mud weight, the field data showed that the P50 threshold did not prevent fluid losses. This study contributes toward better methods to predict shear wave velocities exemplified with field cases in Southeast Iraq --Abstract, page iv