The physical and ecological systems, people, and economy in the Gulf Coast are inextricably linked. The natural system includes processes such as sea-level rise, subsidence, storm surges and flooding, sediment management, marsh and wetland loss, and conservation and restoration activities. The human system encompasses land use and coastal development, adaptation, and migration or relocation. The interactions and feedbacks between the natural and human systems are what make up the coupled system. The committee that carried out the study and wrote the report identified three critical areas of research that could address gaps in high-priority scientific knowledge, helping to inform decision-making and research planning related to the strategic initiatives of the National Academies' Gulf Research Program (GRP): The report recommends that GRP create an integrated research program that focuses on understanding of the evolution of the coupled coastal system.
LOAT is based on incremental automation of the four cognitive functions of interaction. Levels of human/system interaction are described on a nine-point scale ranging from fully manual, to levels of system support for the human, to levels of automation overseen by the human, to full automation. LOAT is a powerful tool for mapping the transition from a purely manual process, to the degree of automation that any system can achieve in the early transition phase, through a timeline to full automation. The transition levels from manual to autonomous were likened to the four cognitive functions that occur in both human interaction with machinery and automated interaction with machinery. These four functions, based on a staged model of human information processing, were translated into equivalent sequential functions applicable to both human processing and automated processing.
Optimal exploitation of hydrocarbon reservoirs has always been a challenge due to uncertainties posed by subsurface heterogeneities that are often not factored into field development plans. Secondary and tertiary recovery mechanisms, such as waterflooding and enhanced oil recovery (EOR), are used to enhance the oilfield recovery beyond primary recovery. However, as the field development transitions to secondary/tertiary mechanisms, the challenges in monitoring these mechanisms further increase the uncertainty in field development. If these uncertainties are not reduced or incorporated properly, the field development may easily become uneconomic. This work presents a workflow that addresses the limitation of regular waterflood surveillance while characterizing the reservoir for optimal exploitation.
The current technologies for waterflood surveillance are limited either to local surveillance methods, such as tracers, crosswell seismic and crosswell electromagnetics (EM), or to uncalibrated global realizations, such as full-field streamline simulation, with no validation between the wells (It is to be noted that a full-field reservoir simulation calibrated with production-injection data in defined time-interval is stated as a global-surveillance method in this paper). This workflow devises integration of an effective local waterflood monitoring method, crosswell EM, and a global waterflood modeling method, streamline simulation. The process of validating the parameters of a geological model and a dynamic model with time-lapse crosswell EM data significantly reduces reservoir characterization uncertainty and helps in the preparation of a precise dynamic model.
You have access to this full article to experience the outstanding content available to SPE members and JPT subscribers. To ensure continued access to JPT's content, please Sign In, JOIN SPE, or Subscribe to JPT LOAT is based on incremental automation of the four cognitive functions of interaction. Levels of human/system interaction are described on a nine-point scale ranging from fully manual, to levels of system support for the human, to levels of automation overseen by the human, to full automation. LOAT is a powerful tool for mapping the transition from a purely manual process, to the degree of automation that any system can achieve in the early transition phase, through a timeline to full automation. The transition levels from manual to autonomous were likened to the four cognitive functions that occur in both human interaction with machinery and automated interaction with machinery.
Several variants of rate-state equations were considered inapplication to description of laboratory data on block slidingunder normal and shear stresses. Both acoustic emission andstick-slip motion of the block were registered and consideredas an analog of ordinary and slow earthquakes. Various typesof fluids were added to the filler between the moving andstationary blocks. Obtained results on the block sliding werecompared with numerical simulations which were conductedusing several variants of the rate-state equations, and the bestmatching model was chosen for further study. With the helpof that model, the seismic activity induced by fluid injectionduring Basel project was simulated. It was shown, that somelong-term aftereffects of the fluid injection can be explainedby specific value of the interblock stiffness related to thesurround rock stiffness.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 210C (Anaheim Convention Center)
Presentation Type: Oral
Improved understanding of the coupled natural/human coastal system will help promote resilience of coastal communities and ecosystems under rapidly changing environmental conditions and support informed decision-making, says a new report from the National Academies of Sciences, Engineering, and Medicine. The physical and ecological systems, people, and economy in the Gulf Coast are inextricably linked. The natural system includes processes such as sea-level rise, subsidence, storm surges and flooding, sediment management, marsh and wetland loss, and conservation and restoration activities. The human system encompasses land use and coastal development, adaptation, and migration or relocation. The interactions and feedbacks between the natural and human systems are what make up the coupled system.
ABSTRACT: The growth of fractures within a quasi-brittle rock is computed numerically with the aim of generating high-density geomechanically realistic three-dimensional discrete fracture patterns. Patterns are generated with a finite element-based discrete fracture propagation simulator, in which deformation and flow are numerically computed. These detailed multi-fracture growth simulations study the emergence of patterns as a function of the interaction of fractures and the mechanical effects of pattern evolution on the distribution of apertures in response to in situ stresses.
The growth of multiple interacting fractures is instrumental in understanding how fracture patterns evolve across scales, and how fracture geometry and topology evolves within a group during mixed mode loading. In particular, realistic modeling of hydraulic fracturing is of great interest. However, fracture growth is relevant to a range of other applications, including hydro-geo-mechanical modeling of processes occurring in or near reservoirs, mines, and nuclear waste repositories.
Fracture growth within quasi-brittle rocks involves propagation and interaction across scales. Growth begins from inter-granular and intra-granular micro-fractures, which have been observed to self-organize in a process whereby ‘initially isolated fractures grow and progressively interact, with preferential growth of a subset of fractures developing at the expense of growth of the rest’ (Hooker et al., 2017). Hooker et al. examine over sixty sandstone samples from five different sites, and conclude that the location of fracture clusters is not random but rather controlled by the interaction mechanics of growing fractures. Thus, the authors suggest that initial micro-fracturing in rocks is not random (e.g. Tang et al. 2018) but rather self-organized, leading to scale-dependent mechanisms for selforganization of fractures at larger scales. The formation of dense fractures systems across scales, outside of layer-restricted systems, is briefly investigated in this work.
Modeling the growth of dense fracture networks is challenging, and in general, fracture density is bound to the observation scale, and it must be assumed that a large number of both smaller and larger scale fractures are also present. A range of numerical methods have been applied to model fractures and fracture growth. These including the finite element method (FEM), the extended finite element method (XFEM), the discrete element method (DEM), combinations of FEM and DEM, discontinuous deformation analysis (DDA), the perturbation method, mesh-less, phase-field. These model the growth of multiple fractures both in 2D and 3D, with or without flow. A partial review of these methods can be found in (Lisjak & Grasselli, 2014).
Managed Pressure Drilling (MPD) enables safer operations and reduces nonproductive time and thus provides the opportunity to reduce well costs. Many operators however, are not fully embracing the opportunity offered by the technology, due to strict regulatory requirements, and their perception that MPD is complicated and increases risk. A basis for this perception is that operator engineers in their designs, and contractor drilling crews in day-to-day operations, are insufficiently MPD experienced to fully and safely exploit the benefits of the technology. Today, the advanced, and field proven, engineering (mathematical) models that are part and parcel of the software used in well design are also available in state of the art simulators. These engineering models and model based simulators are used by engineers during the design phase of MPD projects to replicate the conditions that will be encountered during actual well construction; picking optimal casing setting depths, using different equipment setups, selecting optimal drilling parameters and demonstrating the integrity of the design under a wide scenario of well conditions.
Human Systems Integration (HSI) is a multidisciplinary application of expertise that is heavily relied on in many domains including the military, aerospace and aviation. HSI optimizes the effectiveness of human interaction with the machine and ensures that technology, from development to acquisition, is focused on the requirements of the operator. HSI is finding wider adoption in heavy industrial applications due to its emphasis on improving operator safety and efficiency. HSI is a relatively new concept to drilling operations and has the potential to support significant improvements in performance and successful adoption of drilling systems automation.
Sensing, analyzing and taking action is a similar series of steps for humans and for automated systems. The transition from manual to autonomous operation can be described in a formal taxonomy of discrete levels of automation. Such taxonomy can be adopted for drilling systems automation application. Notably, automation will advance more rapidly in locations not suited to human control as well as for feedback loops requiring continuous and high-frequency adjustment. Improvement in the feedback loop and display to the driller in manual mode will improve drilling performance through a reduction of both Non Productive Time (NPT) and Invisible Lost Time (ILT). Carefully designed controls and system indicators within the operator workspace will enable the human to achieve maximum performance. We describe best practices for applying HSI principles to the operations controlled by drillers, directional drillers and other operators at varying levels of automation, specifically addressing human factors engineering, manpower/personnel, training, operational constraints of the drilling environment, occupational health and safety as well as pertinent organizational factors.
We demonstrate how managing change through the adoption of the aforementioned best practices in the drilling to human interface is critical to success as automation is advanced.
The positive impact of feeding back performance data, in a constructive manner, to the drill crew has been demonstrated through fleet-wide improvements in drill pipe connection times. The development of effective displays has been well advanced in aviation and aerospace and is now being adopted in design of automobiles, locomotive cabs, hospitals, nuclear power plants and many other applications. The application of HSI expertise to the design and implementation of the driller's console, such that it provides the appropriate type operational feedback to the driller, will enhance drilling performance and provide an effective implementation of automation. Further, the application of adaptive automation will optimize the workload of the driller.
Introduction of HSI to the drilling industry, articulation of the Levels of Automation Taxonomy and a roadmap to provide significant improvement in the feedback to the driller in both manual and automation modes will be vastly beneficial to the drilling industry.
We implemented the object-based method using marked point processes to generate a natural fracture network honoring an assumed fracture characteristics’ distribution where the fractures are two-dimensional zero-thickness circular disks. Fractures are divided into two groups based on their alignment which is acquired by Monte Carlo sampling from two Gaussian distributions with 90-degree shift in the mean value; this hypothesis is validated considering the frequently observed checker-board fracture patterns in the outcrops. The growth of the fractures in the second group or the secondary (daughter) fractures can be terminated by a criterion derived from the distribution of the fractures in the first group or the primary (parent) fractures. For data assimilation purposes, a smooth seismic distribution for fracture density is mimicked by simple krigging which inherently possesses a smoothing nature. Then, the generated seismic data is honored by revising the fracture distribution such that in areas with less fracture density we have fewer fractures. This work provides a novel, yet easy and fast workflow to stochastically model a natural fracture network following the attributes offered by seismic data and concludes an orthogonal or bidirectional fracture pattern. This pattern can be easily extended for multi-directional fracture patterns using the proposed framework.
One of the big concerns in fractured reservoirs characterization is the representation of the subsurface fractures due to large uncertainty and extremely limited direct measurements pertaining to exact spatial distribution of fractures. Stochastic approaches allow us to realize fractures discretely . This approach embraces three different methods, object based simulation, hierarchical fracture modeling, and multiple point statistics based algorithms . The current work concentrates on the first method.