Production of natural gas and other hydrocarbons from unconventional sources involves hydraulic fracturing and horizontal drilling, to establish connectivity and increase the permeability. The current recovery rates are only 10–30 percent with the production rapidly declining in the first couple of years. This inefficient extraction leads to drilling of multiple wells, which in turn increases the environmental footprint. The hypothesis is that there are several transport mechanisms that are responsible for hydrocarbon recovery, and a fundamental physics-based understanding of these mechanisms and the production curves will provide information on improving recovery efficiencies. The primary objective of this project is to develop a combined process-level and systems-level reservoir-scale modeling approach that will isolate the key process parameters and their influence on the production curve. This reservoir-scale modeling approach will be built on high-performance computing tools that have been developed at DOE national laboratories.
Los Alamos National Laboratory (LANL), Los Alamos, NM 87545
Unconventional hydrocarbon reservoirs (e.g., tight shale) have naturally existing fractures with very low matrix permeability (nanodarcy). Discrete Fracture Network (DFN) approach, where fractures are modeled as two dimensional planes in three dimensional planes, is known to be an effective approach in characterizing such reservoirs, provided the fracture stochastics are known. The process-level part of the approach is based on dfnWorks, which is a workflow built on the DFN approach. This workflow involves a DFN generator dfnGen, meshing toolkit LaGriT, flow simulator PFLOTRAN, and a particle-tracking toolkit dfnTrans. The challenge with this approach is characterizing fractures at smaller scale (damage) and other smaller scale processes such as matrix diffusion and desorption. Using the systems-level decision support toolkit (MADS) in the framework, the aim is to characterize these smaller scale phenomena using site data (geology, fractures) and production data from different sites (for instance, using data from collaborator Apache Corp and from Texas Railroad Commission). This approach can lead to multiple solutions for the process parameters when calibrating with the site production data. The results from the other two LANL projects (PIs: Xu and Carey) will enable further constraining some of these parameters (e.g., diffusion coefficient from Xu’s project). Additionally, using the combined process-level and systems-level framework, the researchers will be able to perform sensitivity analysis and identify how the production curves depend on the transport process parameters.
The tasks in place will help to identify and isolate the key process parameters, and analyze how the production curve depends on these parameters.
The first phase focused on capability development – implementing the small scale processes into the flow simulator PFLOTRAN, implementing multi-phase flow for flow-blocking analysis and implementing the systems-level decision support toolkit, MADS, into the overall framework with dfnWorks. The second phase is focused on using the combined process-level and systems-level framework to perform calibration on the various site datasets (from literature, Apache Corp, and Texas Railroad Commission), perform sensitivity analysis on the process parameters, identify the key parameters, and then summarize recommendations for improving production efficiency.
The accomplishments to date are as follows:
As of FY19 Q3 the examination of large-scale fracture controls on hydrocarbon production in the Marcellus shale were completed. This includes the impact of the fracture-network geometry/topology; the impact of fracture-network properties, and;the impact of density of fracture stages on production from a natural fracture network.
The effort to compare the Los Alamos DFN with conventional approaches, and to identify key gaps in understanding the contribution of tributary zones and matrix processes has been completed with accomplishments listed above in numbers 8, 9, and 10.
LANL has built a meshing algorithm to integrate their discrete fracture network (DFN) model and the surrounding matrix that is usually not included the DFN mesh. This was then used on the DFN model for evaluating shale production and called the DFNM model. In the DFNM model, the porosity/permeability in the grid cells then change based on the pore pressure.
A review of matrix properties and processes has been discussed with industry partners who have
noted that matrix processes are a likely explanation to the observed production variability in many wells because the nature of hydrocarbon storage in heterogeneous nanopores varies among different shale lithologies. Therefore, the matrix scale likely holds the key for determining ultimate amounts of gas/oil-in-place (GIP and OIP) and for long-term hydrocarbon production after the initial flush (1–10 year time frame).
This project is part of a larger three-pronged examination of a mechanistic approach to analyzing and improving unconventional hydrocarbon production. When all aspects of the larger project are complete, a final report will be generated .
Phase Budget Period 1 – DOE Contribution: $700,000
Phase Budget Period 2 – DOE Contribution: $700,000
Pahes Budget Period 3 – DOE Contribution: $800,000
Budget Period 4 – DOE Contribution: $973,100
Budget period 5 – DOE Contribution: $800,000
Planned Total Funding:
DOE Contribution: $3,973,100Contact
Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production - Part 2: Reservoir-Scale Fractured Systems Modeling (Aug 2018)
Presented by Satish Karra, Los Alamos National Laboratory, 2018 Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA
Mechanisms in Natural Gas Production Using Reservoir - Scale Modeling (Aug 2017)
Presented by Satish Karra, Los Alamos National Laboratory, 2017 Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA
1The original FWP was designated FE-406/408/409-14-FY15 and lasted two budget periods through 3/0216. Subsequent FWPs that extended this research include FE-722-16-FT18 for budget period 3 through 3/2018; FE-954-18-FY18 and FE-954-18-FY18 R1 for budget period 4 through 12/20; and FE-954-20-FY21 for budget period 5 through 9/21.