Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

Research on Seepage Control of Jurong Pumped Storage Hydroelectric Power Station

Based on the geological and hydrogeological conditions of the Jurong Pumped Storage Hydroelectric Power Station (JPSHP), a 3D groundwater flow model was developed in the power station area, which took into account the heterogeneity and anisotropy of fractured rocks. A control inversion method for fractured rock structural planes was proposed, where larger-scale fractures were used as water-conducting media and the relatively intact rock matrix was used as water-storage media. A statistical method was used to obtain the geometric parameter values of the structural planes, so as to obtain the hydraulic conductivity tensor of the fractured rocks. Combining the impermeable drainage systems of the upper storage reservoir, underground powerhouse and lower storage reservoir, the 3D groundwater seepage field in the study area was predicted using the calibrated model. The leakage amounts of the upper storage reservoir, powerhouse and lower storage reservoir were 710.48 m3/d, 969.95 m3/d and 1657.55 m3/d, respectively. The leakage changes of the upper storage reservoir, powerhouse and lower storage reservoir were discussed under the partial and full failure of the anti-seepage system. The research results provide a scientific basis for the seepage control of the power station, and it is recommended to strengthen the seepage control of the upper and lower storage reservoirs and the underground powerhouse to avoid excessive leakage and affect the efficiency of the reservoir operation.

ANALYSIS OF THE METHOD OF DETERMINING THE OPTIMAL VOLUME OF THE STORAGE RESERVOIR FOR THE POWER COMPLEX AS PART OF THE WES-PSPP

The article considers the peculiarities of using energy complexes as part of the WPP-PSPP. It is noted that the most relevant and promising will be the use of such energy complexes in environmentally unfavorable regions that are not among the priorities for the development of wind energy. However, the energy complexes as part of the WPP-PSPP are not currently widespread, one of the reasons is the lack of methods for calculating the optimal parameters of the energy complexes, which should include calculations to determine the volume of the storage reservoir of the energy complex. As a result of solving problems using the analysis of methods of economic effi ciency, methods of ecological justifi cation for autonomous power supply systems, a methodology was developed to determine the optimal volume of the storage reservoir for the energy complex as part of the WPP-PSPP, the main provisions of which are described in this article. The presented methodology allows you to choose the most rational location and shape of the accumulating reservoir on the ground, to fi t the outline of the reservoir into natural conditions, as well as to optimize the ratio of its depth of work and cross-sectional area.

Underground Gas Storage Process Optimisation with Respect to Reservoir Parameters and Production Equipment

The storage of natural gas in geological structures such as depleted fields, aquifers and salt caverns plays an important role in a gas supply system as it balances the fluctuation of gas demand and price. Hydraulic loss due to fluid flow through gas storage production equipment and an interfering effect from nonequal productivity index of storage wells may have an important influence on gas storage performance. An integrated mathematical model is developed based on underground gas storage facility production data. Using this model, the hydraulic loss is determined. A real test case that consists of a gas storage reservoir linked to the surface facility is analysed. The mathematical model uses an experimentally determined pressure drop coefficient in chokes. The base case scenario created using real gas storage facility data enables the achievement of a good history match with the given parameters of the gas storage reservoir. Using the history match simulation case as an initial scenario (a base case), two different scenarios are created to determine the injection and withdrawal performance of the gas storage field. The results indicate that the pressure drop in chokes, when fully open as a constraints in an underground gas storage facility, has a significant impact on gas storage operations and deliverability.