A Discrete Fracture Network (DFN) model was used to simulate the results of a production test carried out in a well drilled in a tight, fractured carbonate reservoir. Several static DFN models, depicting different geological scenarios, were built based on data from well logs, core analyses, PLT surveys and structural geology studies. Each of these models underwent a validation procedure, consisting of the simulation of the production test. The comparison between the simulated results and the actual data identified the scenario whose results most closely matched the actual well behaviour. In order to compensate for the lack of geological data, an iterative loop was performed between the static model and the dynamic simulation. Constraints-added flow simulations provided new information for use in modifying the DFN model, resulting in a step-by-step updating of the static model itself. Finally, a geologically sound model accurately matching the results of the production test was obtained. The final DFN model was used to calculate the equivalent petrophysical parameters that were transferred to the corresponding region of the full field dual-porosity fluid flow model.
In the oil industry, most of the reservoir studies are routinely run using commercial reservoir simulators developed about 30 years ago. These simulators were conceived for serial computers, logically structured grid with moderate permeability contrasts and moderate physical complexity; parallel computing only came in later stages. This is crucial during the reservoir model construction: computational time prevents real-life simulation grids from hosting more than one or two millions of active cells thus, grid coarsening and property upscaling still play a key role. Often, this acts as a hard constraint in EOR/IOR simulation which usually demands increasing accuracy to resolve heterogeneity and structure complexity. In the last decade many reservoir simulators have been specifically developed for parallel architectures, adopting flexible formulations and more robust linear solvers. This provides opportunity to drastically speed-up reservoir simulations, handling large models without upscaling. The next step is to use new simulators as enablers to unlock accurate high resolution models. In this framework, the Company is implementing a step-change in reservoir simulation, deploying a new generation high-resolution simulator for the most critical and complex assets. At the same time, the Company put into operation a 4 Pflop/s High Performance Computing (HPC) system to more effectively support exploration and reservoir activities. This paper documents a study carried out to highlight the benefits to reservoir modeling attained combining the high-resolution reservoir simulator and the top-class HPC facility. Main target of the study was to provide indications about the size of the models that these new technologies allow to manage in real-life applications, where model complexity may be due to geology, number of wells or field development schemes and physical processes. For this purpose, two case-studies were selected, namely a brown and a green field. The brown field is a gas-condensate carbonate reservoir with 30 years of history and 25 years of forecast, both periods characterized by large number of wells and a mix of primary depletion and gas injection. The green field, instead, is a very heterogeneous, faulted, undersaturated oil reservoir with a 15-years development plan based on water flooding. Several reservoir models, from millions to hundreds of millions active cells, were built and simulated in different parallel configurations. The components of the work-flow, modeling packages, simulator and HPC configurations, were stretched to run large scale models within typical reservoir study time-scales, where simulations are expected to last less than one day. The models were deliberately chosen to be larger and more complex than those currently used operationally with the purpose of identifying potential limitations imposed by hardware and software which may impact future generations of simulation models, thus ensuring that the simulator development, model development and hardware requirements are aligned. The present work indicated that models with tens of millions of cells can now be easily simulated by combining HPC systems and high-resolution reservoir simulators. However, moving Giga-cell reservoir models from papers to engineer's desk will require further improvements in simulation technology, with emphasis on scalability and optimal management of multi-core architectures.
The oil industry is developing more and more complex reservoirs, often lying in difficult environments like deep or ultra-deep water. At the same time, brown fields are systematically studied to implement IOR and EOR processes to increase the ultimate recovery factor. In this context one of the challenges is to simulate highly detailed models and provide robust answers for corporate decision making processes. Because conventional reservoir simulators are not very well suited for these purposes, new generation reservoir simulators are tempting solutions. During recent years eni is implementing a step-change in the way reservoirs are modeled and simulated, deploying a new generation, high resolution simulator for the most critical and complex assets. The purpose is twofold: computational efficiency on one side and enabling the development of more accurate models on the other one. The process is run in a selective manner, aimed at identifying opportunities when conventional simulators do not meet expectations. In this paper we present the methodology used by the Company in the selection of field cases, together with the results achieved for some of the most interesting and complex assets. In particular, comparative results, with respect to conventional simulators, are presented for: deep-water reservoirs, a tight oil development, CO2 injection schemes, and a large scale heavy oil project. The analysis is performed using key computational and engineering performance indicators. The deployment is run in cooperation with the technology provider: cases, logic, issues and solutions are discussed together in a critical manner. The process is run on the basis of long term corporate objectives, targeting the simulation of EOR processes and complex assets in a computationally efficient and accurate manner.
The development of any offshore assets is always a technical and economical challenge, especially for the investment decision-maker. It is well known that stand-alone dynamic reservoir models and surface facilities models are not able to effectively capture the overall complexity of the system if considered independently from each other. The task can be effectively faced only coupling together reservoir models, wells, surface network, facilities and processing plants models in the frame of an integrated asset modelling system. Nowadays, however, several solutions and workflows are available to achieve an effective coupling of the surface and subsurface simulation models, and the choice of the right approach can also depend on the specific targets of the study. In this paper, we compare two available solutions for reservoir-network coupling in the case of a real dry gas asset, consisting in a brown field currently in production and five near-by green fields. These green fields will be developed to meet the contractual gas sales target using the spare capacity of the existing facility. Since the near-by fields are currently in different development stages -and then different state of knowledge in terms of data acquisition or reliability of reservoir model -the simulation of the integrated system will need to take into account specific different priorities to correctly balance production from the different fields.Main aim of the activity is to highlight advantages and criticalities of the two different approaches with respect to the specific study goals to be addressed. Simulating multi-asset development scenarios from reservoir to surface facility till the market delivery point represents one of the most important task in the oil and gas business and it can be achieved under two different modeling concepts. The first concept is a simplified solution represented by the capabilities offered by some commercial simulators, where the network solver (based on pre-computed hydraulic table) is directly included in the reservoir simulator together with some possibilities of reservoir coupling. The second approach consists in combining reservoir models with a dedicated network simulator having optimization capabilities, building an Integrated Asset Model (IAM). Although IAM approach is recognized to be an effective tool for asset management, in some cases the simplified approach can be very useful to speed up the screening of different development scenarios without losing accuracy. In the real case considererd in this parer, the production logic was implemented for both methods and applied in the simulation of alternative development scenarios. Both approaches are able to resolve and handle the differences between alternative development scenarios in terms of production profiles, making all the needed information available to the project management team in due time to be able to strengthen the decision making process within an acceptable degree of accuracy.
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