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To ease the modeling missions when analyzing the production performance, we proposed a set of modeling patterns. The production systems own shared behaviors, the so-called modeling patterns, like the singleinput-single-output physical structure and the preventive maintenance strategy. We implement the modeling patterns, which capitalize the modeling knowledge, with the AltaRica 3.0 modeling language. We apply the proposed modeling patterns on a practical offshore installation. The results generated by the modeling patterns agree well with the ones in the literature. Our study indicates that it is beneficial to reuse the capitalized knowledge for modeling the production systems.
The performance of the production system is crucial for the process industry, such as the oil and chemical plants. The production facilities in process industry confront two types of risk. First, the low- probability/high-consequence incidents, like the severe accidents (such as the fire and explosion), are constantly attracting high attention from both industry and society. Second, the high-probability/low-consequence incidents (Signoret, 2010), like the production losses, require further focus from the stakeholders of the production systems.
Several definitions for assessing the production system are commonly utilized. Production performance is the capacity of a system to meet demand for deliveries or performance (NORSOK,1998; ISO, 2008). Production-performance analysis refers to the systematic evaluations and calculations carried out to assess the production performance (NOR- SOK,1998; ISO, 2008). Conducting production-performance analyses contributes to assess the production losses, thus to check if the system complies with the production requirements. Production availability can measure the production performance, which is the ratio of real production to the expected production, or to a reference level, in a period of time (NORSOK,1998; ISO, 2008).
Modeling the production systems is challenging when the systems are complex. It is resource-consuming when the updates of these models are required. The models are expected to be improved by increasing the capability for updating and maintaining the models in the life-cycle of the systems. The high-level modeling languages are alternatives for dealing with these issues.
3D Basin Modeling - A New Tool for Petroleum System Analysis 3D Basin Modeling - A New Tool for Petroleum System Analysis N. Telnaes , C. Zwach and G. Fladmark Norsk Hydro E&P Research Centre, Bergen, Norway Abstract. 2.5 D basin modeling, pseudo 3D modeling or Map View modeling is today used extensively in the oil industry for prospect evaluation and appraisal. Most of these tools are strictly determininstic, while the uncertainties in many of the input parameters require stochastic modeling techniques. These pseudo 3D models are frequently linked with simplified fluid flow simulators based on ray tracing or percolation. The use of 3D basin simulators as an exploration tool has been limited by the trade off between computing times and geologically realistic grid sizes. The paper outlines some of the ways around this and our experience in this field. With increasing use of 3D seismic in an exploration phase it is important to develop 3D basin modeling tools that can utilize these data in a cost effective way. computing times for this to be developed into a
useful exploration tool, the grids used are too coarse to realistically simulate secondary The term "Basin Modeling" is today migration. This is due to the high sensitivity of mostly associated with the simulation of the the migration to sediment properties and thermal history of a basin, the generation and geometric effects. expulsion of hydrocarbons and the migration, With increasing focus on prediction on accumulation and leakage of these pressure and GOR in mature exploration areas, hydrocarbons. 1D- and 2D basin modeling is the need for a true 3D basin simulator is today widely used in the oil industry during increasing. prospect generation and evaluation to define In this paper, we will show examples of and study critical geological factors. 1D Basin the development of basin simulators for Modeling was first described in the early petroleum system analysis from 2.5 D eighties, while 2D Basin modeling became modeling to a feasible and useful true 3D basin common in the early nineties. With 2D Basin simulator which is now being used in a true Modeling, the simulation of pressure and the exploration environment. We will also discuss use of basin simulators for overpressure some of the emerging trends and developments prediction has become common, and may in this field. develop into an industry standard. To investigate basin modeling results spatially in prospective areas, mapping routines are attractive methods, especially Pseudo 3D Modeling where input data are too sparse to perform full 3D basin modeling. This is often referred to as Map view modeling "map-view modeling", 2.5D modeling or "pseudo 3D modeling". This type of modeling Norsk Hydro has developed an in-house may be combined with the popular ray tracing map-view modeling system, Quick Vol 3D, techniques for modeling of secondary integrating the results from commercially migration (SEMI, (SINTEF Petroleum available
Different numerical methods are used but all are implemented in the frequency domain.
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 165713, "A Critical View of Current State of Reservoir Modeling of Shale Assets," by Shahab D. Mohaghegh, SPE, Intelligent Solutions and West Virginia University, prepared for the 2013 SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 20-22 August. The paper has not been peer reviewed.
The coupling of hydraulic fractures and natural-fracture networks and their interaction with the shale matrix remains a major challenge in reservoir simulation and modeling of shale formations. This article reviews methods used to understand the complexities associated with production from shale to shed light on the belief that there is much to be learned about this complex resource and that the best days of understanding and modeling how oil and gas are produced from shale are still ahead.
Preshale Technology. The phrase “preshale” technology aims to emphasize the combination of technologies that are used to address the reservoir and production modeling of shale assets. In essence, almost all of the technologies used today for modeling and analysis of hydrocarbon production from shale were developed to address issues that originally had nothing to do with shale. As the shale boom began, these technologies were revisited and modified in order to find application in shale.
Conventional Discrete Fracture Network. The most common technique for modeling a discrete natural fracture (DNF) network is to generate it stochastically. The common practice in carbonate and some clastic rocks is to use borehole-image logs to characterize the DNF at the wellbore level. These estimates of DNF characteristics are then used for the stochastic generation of the DNF throughout the reservoir.
The idea of the DNF is not new. It has been around for decades. Carbonate rocks and some clastic rocks are known to have networks of natural fractures. Developing algorithms and techniques to generate DNFs stochastically and then couple them with reservoir-simulation models was common practice before the so-called “shale revolution.”
A New Hypothesis on Natural Fractures in Shale
What are the general shapes and structures of natural fractures in shale? Are they close to those of the stochastically generated set of natural fractures with random shapes that has been used for carbonate (and sometimes clastic) formations? Or are they more like a well-structured and well-behaved network of fractures that have a laminar, plate-like form, examples of which can be seen in outcrops (such as those shown in Fig. 1)?
Shale is defined as a fine-grained sedimentary rock that forms from the compaction of silt and clay-sized mineral particles commonly called mud. This composition places shale in a category of sedimentary rocks known as mudstones. Shale is distinguished from other mudstones because it is fissile and laminated.
This is the aim of 2.5-D seismic media. The theory of 2.5-D wave propagation has its