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Collaborating Authors
Results
An Investigation of Gas Recycling in Fractured Gas-Condensate Reservoirs
Temizel, Cenk (Aera Energy) | Kirmaci, Harun (Consultant) | Tiwari, Aditya (Consultant) | Balaji, Karthik (University of Southern California) | Suhag, Anuj (University of Southern California) | Ranjith, Rahul (University of Southern California) | Wijaya, Zein (HESS) | Zhu, Ying (University of Southern California) | Yegin, Cengiz (Texas A&M University) | El Gazar, Ashraf Lofty (Abu Dhabi Co For Onshore Petroleum Operations Ltd.)
Abstract Condensate banking results from a combination of factors including fluid properties, formation flow characteristics, and pressures in the formation and wellbore. The production performance may suffer provided these factors are not understood at the beginning of field development. Determining the fluid properties can be vital in any reservoir, hence it plays a crucial role in gas-condensate reservoirs where condensate/gas ratio is significant in estimates for the sales potential of gas and liquid. Once reservoir fluids enter a wellbore both temperature and pressure conditions may change, where condensate liquid can be produced into the wellbore but liquid can also drop out within the wellbore. If the liquid falls back down the wellbore, the liquid percentage will increase and may eventually restrict the production. Thus, it is very important for robust reservoir management that each and every control and uncertainty parameter is understood not only in theory but also in practice with solid examples as done in this study. A robust commercial optimization and uncertainty software is coupled with a full-physics commercial simulator that models the phenomenon so as to investigate the significance of major parameters on performance of gas-condensate reservoirs under recycling. Control and uncertainty variables have been investigated via several simulation runs in specified ranges to represent real reservoir and performance conditions rather than theoretical assumptions. This study aims to prepare an insight into the mechanism of gas injection process in reducing gas-well productivity losses due to condensate blockage in the near wellbore region. The main goal of this work is to investigate gas recycling into the reservoir to enhance condensate recovery. The results show the influence of each control or uncertainty variable, leading to a better understanding of management of gas-condensate reservoirs under gas recycling. Impact of fractures is significant and the tornado diagrams illustrate the relative significance of each factor. The results and sensitivities are compared and discussed in light of a comprehensive literature review of recycling gas-condensate reservoirs with different process optimization methods. The significance of all major parameters are outlined using tornado charts to serve as a practical example for optimization of relevant future applications.
- North America > United States (1.00)
- Asia > Middle East (0.93)
- North America > Canada > Alberta (0.68)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.68)
- South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > San Juan Formation (0.99)
- South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > Las Piedras Formation (0.99)
- South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Orocual Field > Carapita Formation (0.99)
- (7 more...)
Improved Optimization Through Procedures as Pseudo Objective Functions in Nonlinear Optimization of Oil Recovery With Next-Generation Reservoir Simulators
Temizel, Cenk (Aera Energy) | Tiwari, Aditya (Pioneer Exploration LLC) | Kirmaci, Harun (Turkish Petroleum Corp.) | Aktas, Sinem (Turkish Petroleum Corp.) | Ranjith, Rahul (University of Southern California) | Zhu, Ying (University of Southern California) | Tahir, Sofiane (ADNOC) | Aminzadeh, Fred (University of Southern California) | Yegin, Cengiz (Texas A&M University)
Abstract Optimization has become a practical component in decision-making processes in field development and reservoir management. Although optimization simplifies decision-making, it harnesses complex equations and formulations that may be computationally expensive to solve. Numerical reservoir simulation adds another dimension to this phenomenon when combined with optimization software to find the optimum defined by an objective function. Considering the fact that current reservoir simulation models are constructed with vast amount of data and if it is coupled with optimization software, computational limits of regular computers can cause not being able to reach the aimed result although the recent technological development allows running huge reservoir models with parallel computing systems. Consequently, it is inevitable to achieve robust and faster results in optimization problems. Predefined objective functions in optimization software when combined with numerical reservoir simulators attempt to maximize the net present value or cumulative oil recovery defined with an objective function, where the objective function can be defined to be multi-objective leading to Pareto sets consisting of trade-offs between objectives. Using an optimization algorithm with predefined objective functions does not provide the flexibility to the physical reservoir fluid flow phenomenon to "maneuver" throughout the iterations of an optimization process. It is necessary to use a more flexible objective function by introducing conditional statements through procedures. In this study an optimization software is combined with a commercial reservoir simulator. Conditional statements implemented in the simulator as procedures help the software/simulator combination operate under pseudo-dynamic objective functions that lead to speed and robustness through trying sets of combinations of parameters, and thus achieving conditions that lead to highest recovery within the given time frame as defined by the conditional statement for the condition for which the simulation run is performed. The procedures feature enables implementation of codes by using conditional statements that act as piecewise objective functions, maximizing the recovery and taking into account the timeframe or condition they belong. A commercial reservoir simulator is used in this study with conditional statements enhancing production in a given timeframe featuring certain conditions. The optimized recoveries with pseudodynamic objective functions provide an enhanced recovery, as compared to that of an optimization case with predefined constant objective function in the optimization software throughout the iterations of the optimization and simulation process.
Surface Web Balance Method A Neural Network-Based Approach to Mitigate Subsidence in Diatomites coupling Surface Deformation and Injection/Production Data
Temizel, Cenk (Aera Energy) | Tiwari, Aditya (Pioneer Exploration LLC) | Aktas, Sinem (Turkish Petroleum) | Putra, Dike (Rafllesia Energy) | Suhag, Anuj (University of Southern California) | Kirmaci, Harun (Turkish Petroleum) | Balaji, Karthik (University of Southern California) | Ranjith, Rahul (University of Southern California) | Tahir, Sofiane (ADNOC) | Aminzadeh, Fred (University of Southern California) | Yegin, Cengiz (Texas A&M University)
Abstract Diatomites are high-porosity, low-permeability reservoirs with elastoplastic properties and high geo-mechanical responsiveness. Despite that, diatomites have great potential for oil recovery. Withdrawal of fluids from the reservoir rock leads to subsidence causing compaction and shear stresses. This disturbed stress distribution results in well failures that causes loss of millions of dollars. Successful maintenance of pressure support through optimum injection/production is key to preventing subsidence to mitigate the risk of well failure and achieve better sweep efficiency for recovery. There have been different approaches to tackle subsidence and well failures in diatomites including the use of โbackpressure methodโ coupled with a neural network to optimize injection-production to โbalanceโ the rock in terms of stress-distribution and thus decrease well failure due to shearing. However, using such methods may mask other problems the well is experiencing, such as, mechanical issues that influence production. Another existing approach, satellite-imaging (InSAR) cannot be used to take real-time actions that is crucial in diatomites. Surface tiltmeter data is collected to undertsand the relationship between injection/production and resulting surface deformation, which also provides information about well-to-well connectivity. A neural network-based approach is followed to determine the nonlinear relationship between surface subsidence/dilation and injection-production. This is then used to build an objective function that works to minimize the differences between well-to-well subsidence/dilation measured by the tiltmeters, by adjusting injection-production for the wells. In this paper, a method that harnesses real-time surface tiltmeter data to adjust injection-production distribution in diatomites to decrease well failures is used beyond the existing applications of surface tiltmeter, such as, in the areas of detection of early steam breach to surface in steam operations and fracture orientation and it provides real-time data for robust reservoir management of such reservoirs where satellite imaging is not effective.
- North America > United States > California > Kern County (1.00)
- Europe (0.94)
- North America > United States > Texas (0.93)
- Geology > Rock Type > Sedimentary Rock > Siliceous Rock > Diatomite (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Goose Creek Field (0.99)
- North America > United States > California > San Joaquin Basin > South Belridge Field > Tulare Formation (0.99)
- North America > United States > California > San Joaquin Basin > South Belridge Field > Diatomite Formation (0.99)
- (10 more...)
Integrated asset modeling (IAM) offers the oil industry several benefits. The next-generation reservoir simulators help achieve faster runtimes, insight into interaction between various components of a development, and can be used as an effective tool in detecting bottlenecks in a production system as well as a constant and more effective communication tool between various departments. IAM provides significant opportunities for optimization of very large or complex infrastructures and life-of-field analysis of production optimization scenarios. Simultaneous modeling of surface and subsurface components helps reduce time and enhances efficiency during the decision-making process which eliminates the requirement for tedious, time-consuming work and iterations between separate solutions of reservoir and surface networks. Beyond this convenience, this technology makes it possible to reach more robust results more quickly using surface-subsurface coupling. The objective of this study is to outline the advantages and the challenges in using next-generation simulators on simulation of multiple reservoirs in integrated asset management. Simultaneous simulation of multiple reservoirs adds another dimension of complexity to the process of integrated asset modeling. Several sub-reservoir models can be simulated simultaneously in large fields comprising sub-reservoirs with complex surface systems, which could otherwise become very tedious to handle. In this study, a next-generation reservoir simulator is coupled with an optimization and uncertainty tool that is used to optimize the net present value of the entire asset. Several constraints and bottlenecks in such a large system exist, all connected to one another. IAM proves useful in debottlenecking to increase efficiency of the thorough system. The strengths and difficulties associated with simultaneous simulation and optimization of multiple reservoirs are compared to the more conventional way of simulating the assets separately, thus illustrating the benefits of using next-generation reservoir simulators during optimization of multiple reservoirs. The results show that simultaneous solution of the surface-subsurface coupling gives significantly faster results than that of a system that consists of separate solution of surface and subsurface. The speed difference becomes more significant when the number of reservoirs simulated is more than one. This study outlines the workflow in setting up the model, the CPU time for each component of the simulation, the explanation of each important item in this process to illustrate the incremental benefits of use of next-generation reservoir simulators in simulating multiple reservoirs with surface facilities taken into account. Although the use of next-generation simulators are becoming more common, solid examples that illustrate the benefits of simultaneous simulation of multiple reservoirs with surface facilities under several different constraints like this study are important to prove the use of such tools where it is more convenient to carry out the optimization in a system that handles decision parameters and constraints simultaneously.
- South America > Venezuela > Trujillo > Maracaibo Basin > Ayacucho Blocks > Ceuta-Tomoporo Field (0.99)
- North America > Mexico > Gulf of Mexico > Bay of Campeche > Sureste Basin > Campeche Basin > Northeast Marine Region > Cantarell Field (0.99)
- North America > United States > Alaska > North Slope Basin > Western North Slope > Colville River Field > Alpine Field > Kingak Formation (0.94)
Integrated Workflow for Robust Reservoir Management through Production Optimization in Reservoirs with Geomechanics-Dependent Permeability
Temizel, Cenk (Aera Energy) | Tiwari, Aditya (Consultant) | Aktas, Sinem Setenay (Turkish Petroleum) | Tuna, Tayfun (University of Houston) | Gok, Ihsan Murat (Schlumberger) | Putra, Dike (Rafflesia Energy)
Abstract Reservoir management in fields with geomechanical issues such as subsidence and dilation due to injection and production activities is challenging where it is crucial to prevent well failures due to resulting shear or compaction stresses that might lead to serious losses owing to well and facility failures and impact success of production operations. A robust understanding of changes in reservoir parameters that influence subsurface recovery is as important to predict the adverse effects of compaction in the reservoir. The objective of this study is to investigate the effects of injection and production not only on compaction and dilation but also on reservoir fluid flow phenomenon including the significance of rock properties on flow due to changes in permeability in order to outline the significance of decision or control parameters of injection/production along with uncertainty parameters of reservoir rock. The results will provide useful in observing the significance of each parameter on production and recovery. In our study, a full-physics commercial numerical reservoir simulator has been utilized where it is coupled with an optimization and uncertainty tool to investigate the significance of decision parameters such as amount and rate of injection and production as well as the uncertainty parameters such as rock properties with different Young's Modulus. Different rock types are assigned in the geomechanics section due to different Young's modulus on each layer. Different constitutive models are assigned on rock layers. Linear elastic model and nonlinear elastic model are employed. Output stresses, subsidence and displacement vector are used to evaluate the influence of injection and production. Effect of production on compaction with decrease in permeability has been observed under different scenarios that include different operating conditions such as rates and different uncertainty parameters such as rock types with unique Young's modulus and permeability. The significance of each parameter is presented to provide a better understanding on reservoir management for reservoirs where geomechanical stresses are crucial in decision making on operational constraints and design. Numerical reservoir simulation with geomechanics option is a convenient tool to predict not only the reservoir performance but also the reservoir stresses occurring due to injection and production. The reservoir model with geomechanics-dependent permeability provides a more realistic forecast for recovery compared to the models where geomechanics is ignored. The understanding of effect of injection and production on stresses generated in reservoir enables more optimum operation of wells to minimize such effects that may lead to well failure. Injection and production are optimized in a way where stress distributions are more even that lead to more control on geomechanics that results in mitigation of well failures and minimal loss.
- North America > United States > California > Kern County (0.68)
- Asia (0.68)
- Europe > Norway > North Sea > Central North Sea (0.47)
- North America > United States > California > San Joaquin Basin > South Belridge Field > Tulare Formation (0.99)
- North America > United States > California > San Joaquin Basin > South Belridge Field > Diatomite Formation (0.99)
- North America > United States > California > San Joaquin Basin > San Joaquin Valley > South Belridge Field > Tulare Formation (0.99)
- (11 more...)
Abstract Advancing technology in thermal recovery and declining conventional oil resources are leading to the increased application of thermal heavy oil recovery processes. Heavy oil resources in carbonate rock are estimated at 1.6 trillion bbl, one-third of which is in the Middle East, making it a crucial part of the global energy supply. A thorough understanding of reservoir properties (including but not limited to natural fracture systems, fluid saturation, permeability and rock matrix) is fundamental to optimizing recoveries with existing uncertainties, where efficient optimization through simulation can lead to the application of optimum operational decision parameters combined with improved understanding of the significance of such uncertainty parameters. Steam-assisted gravity drainage (SAGD) provides many advantages compared to conventional surface mining extraction techniques and alternate thermal recovery methods. These advantages include significantly greater per-well production rates, greater reservoir recoveries, reduced water treating costs, and dramatic reductions in steam-oil ratios (SORs). However, carbonate reservoirs exhibit varying properties in terms of porosity, permeability, and flow mechanisms. Many other engineering considerations exist for SAGD, including recovery rate, thermal efficiency, capability and economics of drilling horizontal well pairs, steam quality, steam injection rate, steam pressure, sand production, reservoir pressure maintenance, and water intrusion. In this study, such parameters are simulated and optimized by coupling a numerical reservoir simulator with commercial optimization software featuring exploratory, gradient, and direct methods. A dual permeability approach to modeling naturally fractured reservoirs is used. The results are discussed and the significance of both uncertainty and decision parameters is outlined in addition to the added benefits of optimized solutions using various optimization techniques. The results and observations show significant improvement using the optimum combination of decision parameters, such as steam injection rate, pressure, and temperature, to improve recoveries with a good understanding of the significance of uncertainty parameters, including rock and formation properties, in addition to overburden conditions that can affect the SAGD process. Reservoir management requires effective risk management where a sound understanding of uncertainties is fundamental to success. This study outlines all of these parameters in a comparative way, which can be useful in future industry applications. Modeling fractures in carbonates in heavy oil reservoirs is not an easy task because of the modeling and computational difficulties. It is important to have solid thorough examples, including building the model, setting up the optimization workflow of uncertainty, and outlining the results in a comparative manner to enhance understanding for more efficient reservoir management during SAGD processes.
Abstract Started in the late 1800s in the US, water being relatively inexpensive and readily available in large volumes and as being very effective at significantly increasing oil recovery, waterflooding has been the most common secondary recovery method applied throughout the world, contributing to pressure maintenance in the reservoir and displacing the oil phase. While there are several parameters that influence the performance of a waterflood, water quality is one of the important factors as it may cause scaling in the injection wells as well as some formation damage through chemical phenomena such as cation exchange in the reservoir resulting in decreased the recoveries. As waterfloods continue over decades, prevention of scale formation becomes a more significant factor that needs to be properly treated. The precipitation of inorganic scale is a major issue in injecting brines with a high concentration of divalent ions. The scaling tendency of water is highly correlated with the hardness of the injection water. Following corrosion, insoluble iron precipitates can cause damage in injection wells where precipitates can lead to severe reductions in well injectivity. Water needs to be treated in a proper way if the water contains high concentrations of calcium, magnesium or iron. In most waterflood applications, seawater needs to be used and this phenomenon is also an issue when injecting seawater into formations that contains brines with high salinity. In this study, a comprehensive analysis of this common problem is provided by investigating the significance of parameters affecting the severity of the scale through utilizing a seawater scale buildup model to be simulated using a commercial simulator along with an in-depth review of previous studies.
- North America > United States (1.00)
- Europe (0.68)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Constituents > Salts/Sulphates/Scales (0.70)