Katie Boyle

SeTES: A self-teaching expert system for the analysis, design, and prediction of gas production from unconventional gas resources

SeTES is a self-teaching expert system that (a) can incorporate evolving databases involving any type and amount of relevant data (geological, geophysical, geomechanical, stimulation, petrophysical, reservoir, production, etc.) originating from unconventional gas reservoirs, i.e., tight sands, shale or coalbeds, (b) can continuously update its built-in public database and refine the its underlying decision-making metrics and process, (c) can make recommendations about well stimulation, well location, orientation, design, and operation, (d) offers predictions of the performance of proposed wells (and quantitative estimates of the corresponding uncertainty), and (e) permits the analysis of data from installed wells for parameter estimation and continuous expansion of its database. Thus, SeTES integrates and processes information from multiple and diverse sources to make recommendations and support decision making at multiple time-scales, while expanding its internal database and explicitly addressing uncertainty. It receives and manages data in three forms: public data, that have been made available by various contributors, semi-public data, which conceal some identifying aspects but are available to compute important statistics, and a users private data, which can be protected and used for more targeted design and decision making. It is the first implementation of a novel architecture that allows previously independent analysis methods and tools to share data, integrate results, and intelligently and iteratively extract the most value from the dataset. SeTES also presents a new paradigm for communicating research and technology to the public and distributing scientific tools and methods. It is expected to result in a significant improvement in reserve estimates, and increases in production by increasing efficiency and reducing uncertainty.

MeshVoro: A three-dimensional Voronoi mesh building tool for the TOUGH family of codes

Few tools exist for creating and visualizing complex three-dimensional simulation meshes, and these have limitations that restrict their application to particular geometries and circumstances. Mesh generation needs to trend toward ever more general applications. To that end, we have developed MeshVoro, a tool that is based on the Voro++ (Chris H. Rycroft, 2009. Chaos 19, 041111) library and is capable of generating complex three-dimensional Voronoi tessellation-based (unstructured) meshes for the solution of problems of flow and transport in subsurface geologic media that are addressed by the TOUGH (Pruess, K., Oldenburg C., Moridis G., 1999. Report LBNL-43134, 582. Lawrence Berkeley National Laboratory, Berkeley, CA) family of codes. MeshVoro, which includes built-in data visualization routines, is a particularly useful tool because it extends the applicability of the TOUGH family of codes by enabling the scientifically robust and relatively easy discretization of systems with challenging 3D geometries. We describe several applications of MeshVoro. We illustrate the ability of the tool to straightforwardly transform a complex geological grid into a simulation mesh that conforms to the specifications of the TOUGH family of codes. We demonstrate how MeshVoro can describe complex system geometries with a relatively small number of grid blocks, and we construct meshes for geometries that would have been practically intractable with a standard Cartesian grid approach. We also discuss the limitations and appropriate applications of this new technology.

Evaluation of a deposit in the vicinity of the PBU L-106 Site, North Slope, Alaska, for a potential long-term test of gas production from hydrates

Evaluation of a Deposit in the Vicinity of the PBU L-106 Site, North Slope, Alaska, for a Potential Long-Term Test of Gas Production From Hydrates G.J. Moridis, SPE; M.T. Reagan, SPE; K. Boyle, and K. Zhang, SPE, Lawrence Berkeley National Laboratory Abstract As part of the effort to investigate the technical feasibility of gas production from hydrate deposits, a long-term field test (lasting 18-24 months) is under consideration in a project led by the U.S. Department of Energy. We evaluate a candidate deposit involving the C-Unit in the vicinity of the PBU-L106 site in North Slope, Alaska. This deposit is stratigraphically bounded by impermeable shale top and bottom boundaries (Class 3), and is characterized by high intrinsic permeabilities, high porosity, high hydrate saturation, and a hydrostatic pressure distribution. The C-unit deposit is composed of two hydrate-bearing strata separated by a 30-ft-thick shale interlayer, and its temperatrure across its boundaries ranges between 5 and 6.5 o C. We investigate by means of numerical simulation involving very fine grids the production potential of these two deposits using both vertical and horizontal wells. We also explore the sensitivity of production to key parameters such as the hydrate saturation, the formation permeability, and the permeability of the bounding shale layers. Finally, we compare the production performance of the C-Unit at the PBU-L106 site to that of the D-Unit accumulation at the Mount Elbert site, a thinner, single-layer Class 3 deposit on the North Slope of Alaska that is shallower, less-pressurized and colder (2.3 – 2.6 o C). The results indicate that production from horizontal wells may be orders of magnitude larger than that from vertical ones. Additionally, production increases with the formation permeability, and with a decreasing permeability of the boundaries. The effect of the hydrate saturation on production is complex and depends on the time frame of production. Because of higher production, the PBU-L106 deposit appears to have an advantage as a candidate for the long-term test. Introduction Background. Gas hydrates (GH) are solid crystalline compounds of water and gaseous substances described by the general chemical formula G•N H H 2 O, in which the molecules of gas G (referred to as guests) occupy voids within the lattices of ice- like crystal structures. Hydrate deposits occur in two distinctly different geographic settings where the necessary conditions of low temperature T and high pressure P exist for their formation and stability: in the Arctic (typically in association with permafrost) and in deep ocean sediments (Kvenvolden, 1988). The majority of naturally occurring hydrocarbon gas hydrates contain CH 4 in overwhelming abundance. Simple CH 4 - hydrates concentrate methane volumetrically by a factor of ~164 when compared to standard P and T conditions (STP). Natural CH 4 -hydrates crystallize mostly in the structure I form, which has a hydration number N H ranging from 5.77 to 7.4, with N H = 6 being the average hydration number and N H = 5.75 corresponding to complete hydration (Sloan and Koh, 2008). Natural GH can also contain other hydrocarbons (alkanes C n H 2n+2 , n = 2 to 4), but may also contain trace amounts of other gases (mainly CO 2 , H 2 S or N 2 ). Although there has been no systematic effort to map and evaluate this resource on a global scale, and current estimates of in-place volumes vary widely (ranging between 10 15 to 10 18 m 3 at standard conditions), the consensus is that the worldwide quantity of hydrocarbon GH is vast (Milkov, 2004; Klauda and Sandler, 2005; Sloan and Koh, 2008). Given the sheer magnitude of the resource, ever increasing global energy demand, and the finite volume of conventional fossil fuel resources, GH are emerging as a potential energy source for a growing number of nations. The attractiveness of GH is further enhanced by the environmental desirability of natural gas, as it is an energy resource with a significantly lower carbon intensity than coal, oil, or other solid and liquid fuels. Thus, the appeal of GH accumulations as future hydrocarbon gas sources is rapidly increasing and their production potential clearly demands technical and economic evaluation. The past decade has seen a marked acceleration in gas hydrate RD 2010a). Together, these efforts have helped to clarify the dominant issues and challenges facing the extraction of methane from gas hydrates. Classification of Gas Hydrate Deposits and Production Methods. Natural GH accumulations are divided into three main classes (Moridis and Collett, 2004) based on simple geologic features and the initial reservoir conditions. Class 1 settings are composed of two layers: a Hydrate-Bearing Layer (HBL) and an underlying two-phase fluid zone of mobile gas and liquid water. Because the base of the gas-hydrate stability zone (BGHSZ) coincides with the bottom of the HBL, this is the most desirable system as it is the easiest to destabilize and release gas (Moridis et al., 2008a; 2009). Class 2 settings comprise an HBL, overlying a zone of mobile water. Class 3 accumulations are composed of a single HBL, and are characterized by the absence of an underlying zone of mobile fluids. In Classes 2 and 3, the entire HBL may be at or well within the hydrate stability zone. A fourth class (Class 4) is typical of many oceanic accumulations, and involves disperse, low-saturation hydrate (<10%) deposits that lack confining geologic strata and are not targets for production (Moridis and Sloan, 2007).