Numerical simulation and optimisation of IOR and EOR processes in high-resolution models for fractured carbonate reservoirs

Carbonate reservoirs contain more than half of the world’s conventional hydrocarbon resources. Hydrocarbon recovery in carbonates, however, is typically low, due to multi-scale geological heterogeneities that are a result of complex diagenetic, reactive, depositional and deformational processes. Improved Oil Recovery (IOR) and Enhanced Oil Recovery (EOR) methods are increasingly considered to maximise oil recovery and minimise field development costs. This is particularly important for carbonate reservoirs containing fractures networks, which can act as high permeability fluid flow pathways or impermeable barriers during interaction with the complex host rock matrix. In this thesis, three important contributions relating to EOR simulation and optimisation in fractured carbonate reservoirs are made using a high-resolution analogue reservoir model for the Arab D formation. First, a systematic approach is employed to investigate, analyse and increase understanding of the fundamental controls on fluid flow in heterogeneous carbonate systems using numerical well testing, secondary and tertiary recovery simulations. Secondly, the interplay between wettability, hysteresis and fracture-matrix exchange during combined CO2 EOR and sequestration is examined. Finally, data-driven surrogates, which construct an approximation of time-consuming numerical simulations, are used for rapid simulation and optimisation of EOR processes in fractured carbonate reservoirs while considering multiple geological uncertainty scenarios

Automated Optimization Strategies for Horizontal Wellbore and Hydraulic Fracture Stages Placement in Unconventional Gas Reseroirs

In the last decades rapid advances in horizontal drilling and hydraulic fracturing technologies ensure production of commercial quantities of natural gas from many unconventional reservoirs. Reservoir management and development strategies for shale and tight gas plays have evolved from ad hoc approaches to more rigorous strategies that involve numerical optimization in presence of multiple economic and production objectives and constraints. Application of an automated integrated optimization framework for placement of horizontal wellbores and transverse hydraulic fracture stages along them has potential of increasing shale gas reserves and projects’ revenue even further. This dissertation introduces a novel integrated evolutionary-based optimization framework for placement of horizontal wellbores and hydraulic fracture stages that allows enhancing production from shale gas formations and provides a solid foundation for future field-scale application once better understanding of shale petrophysics and geomechanics is developed. The proposed optimization workflow is developed and tested in stages. First, we summarize what has been done in the subject field previously by scholars and identify what is missing. Second, we present assumptions for the shale gas simulation model that make our framework and the simulation model applicable. Third, we pre-screen several economic and petrophysical parameters in order to identify the most significant for the subsequent sensitivities analysis. Forth, we develop evolutionary-based optimization strategy for placement of hydraulic fracture stages along a single horizontal wellbore. We investigate how sensitive the optimization results to changes in the key parameters pre-selected during pre-screening. Fifth, we enhance the framework to handle multiple horizontal producers, discuss the conditions when such approach is applicable, and extensively test this integrated workflow on a suite of simulation runs. Finally, we implement and apply multi-objective optimization approach (the improved non-dominated sorting genetic algorithm) to the problem of optimal HF stage placement in shale gas reservoirs and analyze the efficiency of our evolutionary-based optimization scheme in presence of multiple conflicting or non-conflicting objectives. Based on our extensive testing and rigorous formulation of the optimization problem, we find that the chosen evolutionary framework is effective in calculating the optimal number of horizontal wells, the number of HF stages, their specific locations along the wells as well as their half-length. We also conclude that further computational efficiency can be achieved if minimum stage spacing and same chromosome elimination procedure are used. The multi-objective approach has been tested on conflicting and non-conflicting objectives and proved to compute the Pareto optimal front of solutions (or production scenarios) in computationally efficient manner

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures

Automated Optimization Strategies for Horizontal Wellbore and Hydraulic Fracture Stages Placement in Unconventional Gas Reseroirs

In the last decades rapid advances in horizontal drilling and hydraulic fracturing technologies ensure production of commercial quantities of natural gas from many unconventional reservoirs. Reservoir management and development strategies for shale and tight gas plays have evolved from ad hoc approaches to more rigorous strategies that involve numerical optimization in presence of multiple economic and production objectives and constraints. Application of an automated integrated optimization framework for placement of horizontal wellbores and transverse hydraulic fracture stages along them has potential of increasing shale gas reserves and projects’ revenue even further. This dissertation introduces a novel integrated evolutionary-based optimization framework for placement of horizontal wellbores and hydraulic fracture stages that allows enhancing production from shale gas formations and provides a solid foundation for future field-scale application once better understanding of shale petrophysics and geomechanics is developed. The proposed optimization workflow is developed and tested in stages. First, we summarize what has been done in the subject field previously by scholars and identify what is missing. Second, we present assumptions for the shale gas simulation model that make our framework and the simulation model applicable. Third, we pre-screen several economic and petrophysical parameters in order to identify the most significant for the subsequent sensitivities analysis. Forth, we develop evolutionary-based optimization strategy for placement of hydraulic fracture stages along a single horizontal wellbore. We investigate how sensitive the optimization results to changes in the key parameters pre-selected during pre-screening. Fifth, we enhance the framework to handle multiple horizontal producers, discuss the conditions when such approach is applicable, and extensively test this integrated workflow on a suite of simulation runs. Finally, we implement and apply multi-objective optimization approach (the improved non-dominated sorting genetic algorithm) to the problem of optimal HF stage placement in shale gas reservoirs and analyze the efficiency of our evolutionary-based optimization scheme in presence of multiple conflicting or non-conflicting objectives. Based on our extensive testing and rigorous formulation of the optimization problem, we find that the chosen evolutionary framework is effective in calculating the optimal number of horizontal wells, the number of HF stages, their specific locations along the wells as well as their half-length. We also conclude that further computational efficiency can be achieved if minimum stage spacing and same chromosome elimination procedure are used. The multi-objective approach has been tested on conflicting and non-conflicting objectives and proved to compute the Pareto optimal front of solutions (or production scenarios) in computationally efficient manner

DECISION SUPPORT FOR OPTIMAL WELL PLACEMENT, INFRASTRUCTURE INSTALLATION AND PRODUCTION PLANNING IN OIL FIELDS

Ph.DDOCTOR OF PHILOSOPH

Integrating geological uncertainty and dynamic data into modelling procedures for fractured reservoirs

Modelling, simulating and characterising flow through naturally fractured reservoirs is a multi-disciplinary effort. The scarcity of data combined with the additional layer of complexity that fractures add to a reservoir makes an efficient integration of all available data fundamental. However, the vast range of data types to be considered and the multitude of disciplines giving their input often results in communication barriers and individuals working within their comfort area, creating further challenges for uncertainty propagation. It is however critical for decision-making to develop geologically consistent reservoir models that recognise the challenges of simulating flow through systems with high permeability and scale contrasts and address the need for an ensemble of reservoir models to sufficiently cover geological uncertainties and their impact on fluid flow. In this work I developed several workflows for naturally fractured reservoir modelling that invite cross-disciplinary thinking by integrating geological uncertainties and dynamic data into the modelling procedure and foster ensemble modelling from the start. The workflows are tested on a synthetic field that is based upon a conceptual model for fold-related fracture distributions. The first workflow involves the use of multiple-point statistics to efficiently model reservoir-scale fracture distribution by upscaling discrete fracture networks and converting them into training images. To cover the impact of fracture-related geological uncertainties on fluid flow efficiently, flow diagnostics were used to screen and afterwards cluster and select training images according to their flow response for further reservoir modelling. The second workflow proposes a novel reservoir modelling technique that considers both static and dynamic data and utilises entropy to generate a diverse ensemble of reservoir models that all match an outset objective. Finally, an agent-based reservoir modelling workflow is introduced, where within a reservoir model, independent but interacting agents follow a set of rules to generate reservoir models that take into account geological prior information and expected dynamic flow responses to drive modelling efforts. Overall, we demonstrated that combining approaches from various disciplines into cross-disciplinary workflows provides great potential for subsurface characterisation. What workflow to adopt within a project, depends on various boundary conditions. The availability of data and time, the confidence in the understanding of the reservoir and the ultimate goal behind the modelling exercise. These factors can impact whether moving along with the simpler, more parametric multiple-point statistics workflow, the entropy-driven workflow that utilises static and dynamic data or the more data-driven agent-based modelling workflow is the right choice.James Watt Scholarshi

Stochastic Parameter Estimation of Poroelastic Processes Using Geomechanical Measurements

Understanding the structure and material properties of hydrologic systems is important for a number of applications, including carbon dioxide injection for geological carbon storage or enhanced oil recovery, monitoring of hydraulic fracturing projects, mine dewatering, environmental remediation and managing geothermal reservoirs. These applications require a detailed knowledge of the geologic systems being impacted, in order to optimize their operation and safety. In order to evaluate, monitor and manage such hydrologic systems, a stochastic estimation framework was developed which is capable of characterizing the structure and physical parameters of the subsurface. This software framework uses a set of stochastic optimization algorithms to calibrate a heterogeneous subsurface flow model to available field data, and to construct an ensemble of models which represent the range of system states that would explain this data. Many of these systems, such as oil reservoirs, are deep and hydraulically isolted from the shallow subsurface making near-surface fluid pressure measurements uninformative. Near-surface strainmeter, tiltmeter and extensometer signals were therefore evaluated in terms of their potential information content for calibrating poroelastic flow models. Such geomechanical signals are caused by mechanical deformation, and therefore travel through hydraulically impermeable rock much more quickly. A numerical geomechanics model was therefore developed using Geocentric, which couples subsurface flow and elastic deformation equations to simulate geomechanical signals (e.g. pressure, strain, tilt and displacement) given a set of model parameters. A high-performance cluster computer performs this computationally expensive simulation for each set of parameters, and compares the simulation results to measured data in order to evaluate the likelihood of each model. The set of data-model comparisons are then used to estimate each unknown parameter, as well as the uncertainty of each parameter estimate. This uncertainty can be inuenced by limitations in the measured dataset such as random noise, instrument drift, and the number and location of sensors, as well as by conceptual model errors and false underlying assumptions. In this study we find that strain measurements taken from the shallow subsurface can be used to estimate the structure and material parameters of geologic layers much deeper in the subsurface. This can signicantly mitigate drilling and installation costs of monitoring wells, as well as reduce the risk of puncturing or fracturing a target reservoir. These parameter estimates were also used to develop an ensemble of calibrated hydromechanical models which can predict the range of system behavior and inform decision-making on the management of an aquifer or reservoir

Developing tools for determination of parameters involved in CO₂ based EOR methods

To mitigate the effects of climate change, CO₂ reduction strategies are suggested to lower anthropogenic emissions of greenhouse gasses owing to the use of fossil fuels. Consequently, the application of CO₂ based enhanced oil recovery methods (EORs) through petroleum reservoirs turn into the hot topic among the oil and gas researchers. This thesis includes two sections. In the first section, we developed deterministic tools for determination of three parameters which are important in CO₂ injection performance including minimum miscible pressure (MMP), equilibrium ratio (Kᵢ), and a swelling factor of oil in the presence of CO₂. For this purposes, we employed two inverse based methods including gene expression programming (GEP), and least square support vector machine (LSSVM). In the second part, we developed an easy-to-use, cheap, and robust data-driven based proxy model to determine the performance of CO₂ based EOR methods. In this section, we have to determine the input parameters and perform sensitivity analysis on them. Next step is designing the simulation runs and determining the performance of CO₂ injection in terms of technical viewpoint (recovery factor, RF). Finally, using the outputs gained from reservoir simulators and applying LSSVM method, we are going to develop the data-driven based proxy model. The proxy model can be considered as an alternative model to determine the efficiency of CO₂ based EOR methods in oil reservoir when the required experimental data are not available or accessible

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality