SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to increase the efficiency of oil production and to decrease the percentage of oil that is currently inaccessible using bioinformatics software to predict oil well features based on subsurface microbes. Over 50% of domestic oil reservoirs cannot be economically produced today, and a lack of information about the subsurface is a key roadblock to increasing this efficiency. The goal is to help unlock $1 trillion of economic value in domestic energy potential currently inaccessible with current technology. This project seeks to utilize the subsurface microbes as a network of sensors that provide novel information for oil and gas companies. This information will be used to inform key decisions that increase production with more targeted approaches that result in greater U.S. energy independence while reducing the environmental impact of oil drilling and production. In so doing, the project will provide disruptive value in the $12B reservoir data and well logging industry. This SBIR Phase I project proposes to use microbial communities that are native to the subsurface as an advanced and cost effective sensor network that describe key properties of the oil reservoirs. Deep within the subsurface, there are trillions of unique interactions of the native microbes to their ecosystem. By combining advances in cloud computing, DNA sequencing, and novel software analytics, this project will demonstrate that these microbial communities correlate to meaningful production parameters for the oil and gas industry. In so doing, the project will demonstrate at pilot scale that this new information source can be utilized as a novel, non-invasive, low-cost reservoir characterization tool that allows the industry to maximize hydrocarbon production while minimizing environmental impact.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to increase the efficiency of domestic oil production. Using current methods, the oil industry is able to extract only 5% of the known hydrocarbon from shale formation. This represents more than $500B of potential oil and gas that cannot be economically produced for the US economy. The aim is to increase the efficiency of oil production by providing novel subsurface information to improve operational decision making. The resulting value increase for a producer can be up to $1M per well. In addition, it is possible to significantly reduce the environmental impact of the hydraulic fracturing process, which is currently only 50% efficient. By providing novel subsurface data for the industry, this information can reduce environmental impact by saving up to 45B gallons of fresh water and 1M rail cars of mined sand. Furthermore, the analysis of subsurface microbiomes is a rich area for new academic knowledge. Over 80% of the microbial strains identified in Phase I have never been documented in public references. This work not only provides economic and social value, but also expands scientific knowledge.This SBIR Phase II project proposes to use next-generation microbiome analyses to increase the efficiency of domestic oil production. The research objective is to analyze the subsurface hydrocarbon microbiome to characterize hydrocarbon reservoirs and leverage this new data source to increase current efficiency rates. The goal is to analyze 50 producing wells in the Southwestern US, and develop statistical models linking microbial profiles to key reservoir properties that can increase production efficiencies. The analytical method employed will utilize technology stemming from over $20 million in government funding to the University of Colorado used to create bioinformatics software known as "QIIME." The QIIME technology has been extensively tested in analyzing and modeling the human microbiome, but has never been applied to the subsurface hydrocarbon microbiome prior to the Phase I work. By combining advances in cloud computing, DNA sequencing, and novel software analytics, this project will demonstrate that these microbial communities correlate to meaningful production parameters for the oil and gas industry. In so doing, the project will demonstrate at pilot scale that this new information source can be utilized as a novel, non-invasive, low-cost reservoir characterization tool that allows the industry to maximize hydrocarbon production while minimizing environmental impact.