A Case History for an Integrated Asset Model with Fluid Delumping for a Complex Gas Condensate Field

Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility. Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGR values are as following;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model. Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model. In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model enables the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime and will be used to propose new locations and optimize the tie-in location of future wells in addition to providing flow assurance indications throughout the field's life and under different network configurations.

Adoption of Integrated Asset Modeling Approach to Kick-Start the Corporate Digital Transformation Strategy

The adoption of an integrated asset modeling approach was explored to kick-start the corporate digital transformation strategy for its oil and gas section. Besides the integrated asset model, the digital initiatives included predictive maintenance, well performance optimization, and a flow assurance advisor aimed at daily production operations and maintenance, creating a pathway to the digital oilfield (DOF). The integrated asset model would be the main pillar of DOF realization and implementation, its offered technology aimed at short-term, medium-term, and long-term planning. The adopted well-proven integrated asset modeling methodology enabled a geological complex with a high-fidelity physics reservoir model, multiple interdependent wells, pipeline networks, process facility models to be integrated seamlessly on a single platform for validation of its existing production operation strategy and field development plan. The black-oil reservoir model was history matched, and the production network models had detailed wellbore and pipeline hydraulics calibrated with the latest well-test data. The compositional fluid modeling allowed the capture of any flow assurance issues that arose across the networks, which were mapped to the corresponding process facility models with physical specifications and operational constraints defined. A fully integrated asset model was developed for the studied asset, where liquid/vapor tables were prepared for black-oil delumping (Ghorayeb and Holmes, 2005) of the reservoir models to surface network models (Mora et al. 2015), while fluid models of both production network and process models were validated before mapping to ensure fluid fidelity. The availability of this integrated asset model with an embedded spreadsheet program incorporating some simple economic calculations allowed the flexibility of short-term production optimization and long-term asset planning, which was focused to provide all the vital valuable inputs to better field management, fast and accurate decision making, and optimum safe operation of process units in meeting the sales contract. The integrated asset model offered a platform for engineers from different domains to collaborate with aligned common operational and planning objectives. It empowered assessments of production operation strategy and field development scenarios conducted at full field level from pore to process. The customized reporting, the ability to connect to other tools, and to push results to dashboards helped to kick-start the corporate digital transformation strategy.

Effects of Diffusion, Adsorption, and Hysteresis on Huff-n-Puff Performance in Ultratight Reservoirs with Different Fluid Types and Injection Gases

Cyclic solvent injection, known as solvent huff-n-puff, is one of the promising techniques for enhancing oil recovery from shale reservoirs. This study investigates the huff-n-puff performance in ultratight shale reservoirs by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, and black oil) and different injection gases (CO2, C2H6, and C3H8). A dual-porosity compositional model is utilized to comprehensively evaluate the impact of multicomponent diffusion, adsorption, and hysteresis on the production performance of each reservoir fluid and the retention capacity of the injection gases. The results show that the huff-n-puff process improves oil recovery by 4–6% when injected with 10% PV of gas. Huff-n-puff efficiency increases with decreasing gas-oil ratio (GOR). C2H6 provides the highest recovery for the black oil and volatile oil systems, and CO2 provides the highest recovery for retrograde condensate fluid type. Diffusion and adsorption are essential mechanisms to be considered when modeling gas injection in shale reservoirs. However, the relative permeability hysteresis effect is not significant. Diffusion impact increases with GOR, while adsorption impact decreases with increasing GOR. Oil density reduction caused by diffusion is observed more during the soaking period considering that the diffusion of the injected gas caused a low prediction error, while adsorption for the injected gas showed a noticeable error.

A GPU-Based, Industrial Grade Compositional Reservoir Simulator

Recently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs had yet to emerge, and some questions remained as to whether the benefits observed in black-oil simulation would persist with a more complex fluid description. We present our positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state. We describe the motivations for the formulation we select to make optimal use of GPU characteristics, including choice of primary variables and iteration scheme. We then describe performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of scaling with respect to the number of hydrocarbon components and model size, as well as number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth.

A Massively Parallel Reservoir Simulator on the GPU Architecture

Reservoir simulation computational costs have been continuously growing due to high-resolution reservoir characterization, increasing model complexity, and uncertainty analysis workflows. Reducing simulation costs by upscaling is often necessary for operational requirements. Fast evolving HPC technologies offer opportunities to reduce cost without compromising fidelity. This work presents a novel in-house massively parallel full-physics reservoir simulator running on the emerging GPU architecture. Almost all the simulation kernels have been designed and implemented to honor the GPU SIMD programming paradigm. These kernels include physical property calculations, phase equilibrium computations, Jacobian construction, linear and nonlinear solvers, and wells. Novel techniques are devised in various kernels to expose enough parallelism to ensure that the control and data-flow patterns are well suited for the GPU environment. Mixed-precision computation is also employed when appropriate (e.g., in derivative calculation) to reduce computational costs without compromising the solution accuracy. The GPU implementation of the simulator is tested and benchmarked using various reservoir models, ranging from the synthetic SPE10 Benchmark (Christie & Blunt, 2001) to several industrial-scale models. These real field models range in size from tens of millions of cells to more than billion cells with black-oil and multicomponent compositional fluid. The GPU simulator is benchmarked on the IBM AC922 massively parallel architecture having tens of NVidia Volta V100 GPUs. To compare performance with CPU architectures, an optimized CPU implementation of the simulator is benchmarked on the IBM AC922 CPUs and on a cluster consisting of thousands of Intel's Haswell-EP Xeon® CPU E5-2680 v3. Detailed analysis of several numerical experiments comparing the simulator performance on the GPU and the CPU architectures is presented. In almost all of the cases, the analysis shows that the use of hardware acceleration offers substantial benefits in terms of wall time and power consumption. This novel in-house full-physics, black-oil and compositional reservoir simulator employs several novel techniques in various simulation kernels to ensure full utilization of the GPU resources. Detailed analysis is presented to highlight the simulator performance in terms of runtime reduction, parallel scalability and power savings.

A Practical Approach to Expedite a Pore to Process Simulation Model for a Rich Gas Condensate Reservoir – Case Study

Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility. Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGRs as follows;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model. Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model. In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications throughout the field's life and under different network configurations. The model enabled the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime.

Vaca Muerta: An Emerging Shale Petroleum Reservoir

Argentina is ranked second globally in terms of technically recoverable shale gas, and fourth in shale oil (EIA 2015). The most prolific shale is the Vaca Muerta formation. The objective of this paper is to present geological and reservoir characterization, drilling and production strategies, as well as historical performance and economics of Vaca Muerta. The word petroleum as used in this paper includes oil, natural gas, and natural gas liquids. This paper describes natural fractures and their impact on hydrocarbon productivity. The successful commercial production from this unconventional resource has been driven by many factors, including regional geology, availability of advanced technology such as horizontal drilling and multi-stage hydraulic fracturing, as well as domestic and regional hydrocarbon demand (Sierra 2016). Vaca Muerta itself is very unique with multiple hydrocarbon windows from east to west, ranging from dry gas to wet gas, to light oil and black oil. The productivity of Vaca Muerta is benchmarked to some of the best US shale plays such as the Eagle Ford and the Marcellus. Vaca Muerta contains 1202 Tcf of risked gas in-place and 270 billion barrels of risked oil in-place. It is estimated that 308 Tcf and 16 billion barrels of these resources are technically recoverable (EIA 2015). To date, the total number of horizontal wells exceeds 600, mostly drilled in the black oil window (Secretaria de Energia de Argentina 2020; Wood Mackenzie 2020b). Dubbed the ‘golden goose’ of Argentina, the last decade has seen rapid exploration and development activities. The Argentina state oil company (YPF) leads the development in this region together with its partners. In 2019, production from Vaca Muerta reached 90,000 bbl/d of oil and 1180 MMcf/d of gas, contributing 21% of Argentina's total production (Secretaria de Energia de Argentina 2020; Wood Mackenzie 2020b). YPF predicted these rates would increase by 150% in 2022 (Rassenfoss 2018). Part of this increase will be contributed by La Amarga Chica block, where YPF and its partner, PETRONAS approved their 30-year master development plan in late 2018 to deliver 54,000 boe/d by 2022 (Zborowski 2019). This production increase has obviously been delayed due to the COVID-19 pandemic. The novelty of this paper is integration of geological and reservoir characterization, drilling and production strategies, as well as historical performance and economics of Vaca Muerta. It is concluded that oil and gas potential in the Vaca Muerta shale is significant and rivals the potential of some of the shales widely developed in the Unites States and Canada.

A Graphics Processing Unit–Based, Industrial Grade Compositional Reservoir Simulator

Summary Recently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs has yet to emerge, and the question remains as to whether the benefits observed in black-oil simulation persist with a more complex fluid description. We present a positive answer to this question through the extension of a commercial GPU-basedblack-oil simulator to include a compositional description based on standard cubic equations of state (EOSs). We describe the motivations for the selected nonlinear formulation, including the choice of primary variables and iteration scheme, and support for both fully implicit methods (FIMs) and adaptive implicit methods (AIMs). We then present performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of runtime and memory scaling with respect to the number of hydrocarbon components and model size, as well as the number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.