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We found that supercritical CO2 and the availability of protein-rich matter in depleted oil reservoirs can result in the biogenic production of H2 from oil hydrocarbons by indigenous microbial communities. Our experimental results support the hypothesis that a decrease in pH to acidic levels due to the dissolution of supercritical CO2 into the formation water and availability of protein-rich matter favors the activity of H2-producing microbial communities over the activity of H2-using microbial communities. To determine where, when, and how much H2 could be produced in a depleted oil reservoir injected with CO2 and produced water (PW) supplied with protein-rich matter, we simulated the biogenic production of H2 for the Morrow B sandstone reservoir. Simulations were conducted using CO2Bio, a program developed to simulate the multiphase bio-geochemical reactive transport of CO2-CH4-H2-H2S gases in geological carbon storage (GCS) sites. The microbiological capabilities of CO2Bio are validated against batch reaction experimental results. Our field-scale simulation results indicate that 154 – 1673 kg of H2 could be produced after 100 days of CO2 and PW co-injection into a single well of radial flow, and that sandstone reservoirs are more suitable than carbonate reservoirs to produce H2 from dissolved hydrocarbons. Based on the obtained experimental and simulation results, we propose a new H2 production method that couples GCS and PW disposal in depleted oil reservoirs to attenuate environmental and energy issues related to global warming derived from atmospheric pollution with CO2, risk of freshwater resources contamination with PW, and depletion of energy resources.
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Application of Machine Learning Methods to Well Completion Optimization: Problems with Groups of Interactive Inputs.
Abstract In unconventional reservoirs, optimal completion controls are essential to improving well productivity and reducing costs. In this article, we propose a statistical model to investigate associations between shale oil production and completion parameters (e.g., completion lateral length, total proppant, number of hydraulic fracturing stages), while accounting for the influence of spatially heterogeneous geological conditions on hydrocarbon production. We develop a non-parametric regression method that combines a generalized additive model with a fused LASSO regularization for geological homogeneity pursuit. We present an alternating augmented Lagrangian method for model parameter estimations. The novelty and advantages of our method over the published ones are a) it can control or remove the heterogeneous non-completion effects; 2) it can account for and analyze the interactions among the completion parameters. We apply our method to the analysis of a real case from a Permian Basin US onshore field and show how our model can account for the interaction between the completion parameters. Our results provide key findings on how completion parameters affect oil production in that can lead to optimal well completion designs.
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- Award ID(s):
- 1854655
- PAR ID:
- 10341891
- Date Published:
- Journal Name:
- SPE Annual Technical Conference and Exhibition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2118/206174-MS
