Condensate banking is one of the main challenges facing operators of gas condensate reservoirs. Different techniques have been used over the years to improve well deliverability in condensate reservoirs, including gas recycling, gas injection, matrix acidization and hydraulic fracturing. This study investigates design options for effective CO2 injection strategies to reduce liquid blockage and improve productivity in tight reservoirs. We use dynamic compositional models to simulate condensate formation under realistic operating conditions. Initial sensitivity studies are performed using single well models, to design simulation cases that capture the condensate banking problem accurately for selected permeability scenarios. Then full field models are constructed to evaluate the potential of CO2 injection schemes: near-wellbore huff and puff, and field scale continuous injection. Different field development options are investigated, considering the effects of well placement, injected gas volumes, quality of injected gas and frequency of huff and puff cycles. The results of this work show that CO2 huff and puff processes can be highly effective for mitigating condensate drop-out in tight reservoirs, whereas there is likely to be no significant advantage for huff and puff in comparison to continuous CO2 injection in moderately permeable reservoirs. Well spacing and injected volumes can impact recovery significantly: we suggest analytic methods, based on well drainage areas, to estimate suitable values for these parameters. As the production scheme will involve recycling CO2, we consider the effect of impurities in the injection stream: our study indicates that it is not essential to use pure CO2 but sensitivity studies should be performed to check the effects of injected gas composition on recovery. We also demonstrate that recovery from huff and puff operations can be improved by using variable-length cycles, and propose a method to adjust the injection periods dynamically. In this paper, we have identified important parameters that can affect the performance of CO2 huff and puff processes in tight gas condensate fields. The methodology described here provides a guideline for simulation studies of CO2 injection, which will help reservoir engineers to optimize field development and operations.

Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations. To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies. The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity. Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.

The sustained increase in global demand for cleaner fuels continues to drive the gas industry growth. Liquefied natural gas (LNG) has been a key enabler for this growth by making sizeable remote gas re-serves, which are unreachable by pipeline, accessible to the major and emerging gas markets. Every segment of the LNG supply chain has its own set of technical challenges. On the upstream side, many gas resources require significant pre-treatment before liquefaction, and the feed gas to the LNG facility is typically a mixture of various compositions from multiple sources; this composition mix evolves over the life of the project. The main challenge is development planning for the contributing reservoirs under the constraints imposed by the processing facility– managing reservoir deliverability, scheduling & sequencing of wells, and downtime management while maintaining the inlet stream specification. To aid with long-term planning for such assets, a virtual field management system is needed that can emulate a real-world hydrocarbon producing asset by capturing all operational constraints, resource lim-its, and complex operating logic. This paper describes a comprehensive field management framework that can create an integrated vir-tual asset by coupling reservoir, wells, network, and facilities models and provides an advisory system for efficient asset management. The field management component can replicate any operational logic, exercises holistic control over the sub-surface model, integrates with the surface network model, and provides optimization capabilities. This paper demonstrates this for a complex LNG asset that is fed by sour gas of different compositions from multiple reservoirs. We describe the different levels of constraints the asset needs to operate under, including treatment plant capacity, the LNG production capacity, the contractual LNG specifications, the disposal of gas impurities and imposes them on the model by utilizing a flexible and extensible logic framework. Con-straints applied at different levels can be mutually competing and their combination with recovery opti-mization goals increases complexity. The unified field management system uses a robust scheme to bal-ance the coupled system under these constraints while optimizing overall recovery. The optimization is enabled through the ability provided by the field management system to query and retroactively control flow entities during the simulation at the desired frequency. Customization through scripting was necessary to implement this advanced logic and was enabled by the extensible nature of the field management framework. This extensibility, along with native capabili-ties, ensures that any level of complexity can be captured, and the workflow described in this paper can be applied to any hydrocarbon producing asset for short-term and long-term development planning.

Simulating a high-resolution multimillion cell model brings many benefits, by enabling reservoir engineers to use the best grid size for accurate representation of water and gas movement in the reservoir, essential for advanced field management, Enhanced Oil Recovery or complex well design studies. To improve the characterization of a giant heterogeneous carbonate reservoir and enhance the quality of field development plans, new high-resolution static and dynamic models have been used to study one of the largest oil fields in Abu Dhabi. A detailed static model of over 50 million grid cells was constructed, using a unique water saturation modeling approach, without upscaling to a dynamic simulation, using hysteresis for both relative permeability and capillary pressure. The reservoir has over 50 years of history, with hundreds of vertical and horizontal wells. Large volumes of data from well logs, cores and other measurements were used to populate the static model, define dynamic rock types and match well log water saturation and water capillary pressure profiles. The concept of wettability change with depth was introduced, with an oil-wet system at the crest, graduating to a water-wet system near the thin transition zone. A geological resolution grid was used for reservoir simulation studies, after testing input data consistency and stable behavior. A stability test was performed by running the simulation with no wells for 50 years after equilibration and showed no movable fluids. This verified the consistency of the reservoir static properties, rock types, water saturation, relative permeability and fluid model. A history matched case was developed with over 850 wells using the same fine grid, to meet the objective of completing each simulation run within one day. After history matching, a compositional simulation model was built, to investigate the impact of grid resolution on future production forecasts. This is the largest dynamic model built by the company and demonstrates the benefits of rigorous attention to the quality of the static data, while using modern simulation workflows to avoid compromising the detailed model by upscaling. The methodologies presented in this paper will be adopted as best practices for future similar projects.

As the oil and gas industry enters the digital era, openness is a key enabler to realizing the vision of transforming the industry for the better. The practice of reservoir engineering and reservoir simulation is no exception. In this paper, an openness mechanism in a reservoir simulator using Python scripting language is introduced. It empowers engineers to utilize simulation in new ways. It extends simulator capabilities and enables people to implement flexible-control logic to solve field management challenges. The new openness mechanism in the simulator allows engineers to program and include Python scripts in a simulation model. These scripts interrogate and interact with the simulator. The scripts are executed by the simulator while running the model. Flexibility is available to execute the scripts at every Newton iteration, before and after every simulation timestep, at specified times, or when a criterion is met. Simulation model properties can be queried through the scripts, such as well connections, well properties, group properties, grid region properties, network entities, current simulation time, etc. The scripts can set properties such as well constraints, well and connection productivity index (PI), group production target, pausing or stopping the run, etc. Customized control logic, if not directly available in the simulator, can be implemented in the scripts that interrogate and drive the simulator. Such customization can be packaged as Python libraries and shared with team members, enabling continuous value creation. Public Python libraries, such as NumPy, pandas and pywin32 or win32com, can also be loaded in the scripts to extend even further what the simulator can do. The openness mechanism is demonstrated on case examples. They include customized action to acidize wells when production drops, approximating the geomechanical effect of decreasing well pressure, modeling the effect of fines in injected water on well injectivity, and connecting to a network simulator. Examples are also given on customized reporting for model diagnostics and result interpretation, setting production constraints based on economics calculated in an Excel sheet with complex fiscal regime, advanced gas accounting, management of sulphur content, dual-optimization to meet gas demand while honoring oil treatment capacity, and integrated asset modeling from reservoir to surface networks to processing facilities. the ability to extend built-in functionalities of a reservoir simulator and customize field management controls using user scripting language. It embraces innovation and enables continuous value creation in reservoir simulation.