No abstract

No abstract

Power plants and other industries in Oklahoma produce a huge amount of CO2 emissions that should be mitigated for environmental benefits. One method to mitigate these emissions is permanent CO2 sequestration through mineralization. CO2 can be mineralized in the subsurface if injected into iron- magnesium-rich igneous formations that form carbonate minerals. In Southwest Oklahoma, there are several mafic basaltic formations that can be targeted for CO2 storage. The objective of this study is to quantify carbon storage through mineralization in Southwest Oklahoma. In this study, we built a carbon sequestration numerical model to simulate the geochemical reactions of injecting CO2 into a saline aquifer. The model includes three main geochemical reactions: CO2 dissolution in water, dissolution of formation minerals, and precipitation of carbonate minerals. The first reaction results in forming carbonic acid that reacts with the formation minerals: anorthite, wollastonite, pyroxene, and olivine, which results in releasing calcium and magnesium ions. The reaction between free ions in the formation of water and dissolved CO2 results in precipitating carbonate minerals: magnesite and calcite. CO2 is injected into the formation for four years and simulated for the next 200 years. The rate of dissolution and precipitation was monitored as a function of time. In addition, the reservoir parameters: porosity, permeability, and reservoir pressure, were analyzed as a function of time and precipitation rate. The results show that 97% of the injected CO2 is mineralized, and the rest is residually trapped and dissolved in water. Due to the mineralization of CO2 in the form of magnesite, and calcite, the porosity decreased by 5% maximum due to the extra cement in the pore space. The reservoir pressure increases during the injection, but it decreases rapidly after due to the quick CO2 mineralization. Lower reservoir temperature increases the amount of CO2 mineralized due to the higher CO2 solubility in water. In addition, changing the activation energy of mineral reactions leads to a change in the dynamics of CO2 mineralization, but the net of CO2 mineralization changes slightly. The carbon storage numerical model built for this study considers the effect of the formation water chemistry and rocks mineralogy on the amount of CO2 sequestrated. In addition, it shows that Oklahoma can lead to carbon sequestration in basaltic formations.

Flow pattern of a multi-phase flow refers to the spatial distribution of the phase along transport conduit when liquid and gas flow simultaneously. The determination of flow patterns is a fundamental problem in two-phase flow analysis, and an accurate model for gas-liquid flow pattern prediction is critical for any multiphase flow characterization as the model is used in many applications in petroleum engineering. The objective of this study is to present a new model based on machine learning techniques and more than 8000 laboratory multi-phase flow tests. The flow pattern is affected by fluid properties, in-situ flow rates of liquid and gas, and flow conduit geometry and mechanical properties. Laboratory data since 1950s have been collected and more than 8000 data points had been obtained. However, the actual flow conditions are significantly different with any laboratory settings. Therefore, several dimensionless variables are derived to characterize these data points first. Then machine learning techniques were applied on these dimensionless variables to develop the flow pattern prediction models. Applying hydraulic fundamentals and dimensional analysis, we developed dimensionless numbers to reduce number of freedom dimensions. These dimensionless variables are easy to use for upscaling and have physical meanings. We converted the collected data from actual laboratory measurement to the variations of these dimensionless variables. Machine learning techniques on the dimensionless variable significantly improved their predictive accuracy. Currently the best matching on these laboratory data was about 80% using the most recently developed semi-analytical models. Using machine learning techniques, we improved the matching quality to more than 90% on the experimental data. This paper applies machine learning techniques on flow pattern prediction, which has tremendous practical usages and scientific merits. The developed model is better than current existing semi-analytical or classical correlations in matching the laboratory database.

Forecasting production from unconventional reservoirs is a slippery slope that could lead to unrealistic results even though a model has been history matched with production history and vetted by experienced engineers. The reasons for failing conventional decline curves lie in convoluting and heterogeneous reservoir properties, advancing drilling and completion techniques, and dynamic production and operations management. However, the initial rates, declining rate, and ultimate recovery of a well can be viewed as relatively static and predetermined properties of a declining profile. This paper will propose a machine learning-based framework to determine these properties for unconventional reservoir development. In the proposed algorithm, instead of directly data mining on the raw data from different categories and scales, we propose to convert these data into dimensionless variable groups to reduce the dimension of the problem. The dimensionless variables are developed using inspection analysis; most have physical meanings and are easy to upscale. In the case study, we used the production, completion, and petrophysical data to generate new type curves and developed a step-by-step process to explain the aspect of "engineering" code that incorporates physics into the machine learning (ML) process. Dimensionless variables are used in the machine learning process giving physical meaning and reducing the number of predictors, thus improving the speed and efficiency of the code. The results show that the quantity of cumulative oil production over time can be determined using machine learning models with R2 >= 0.90 for individual wells and R2>=0.80 for cross-validated cumulative production forecasts. We can use these determined values to assess the quality of initial rates, declining rates, and ultimate recovery to derive new type curves that incorporate physics and engineering practices. The work emphasizes the importance of accounting for completion parameters, fluid properties and rock quality, thus improving the confidence in results obtained through traditional engineering methods. The machine learning model results provide credibility and support to rates and recoveries for DCA forecasted wells. When modeling hundreds if not thousands of wells, this work shows the importance of utilizing machine learning to harness the power of the data that has been collected on them. The machine-learning-based declining profile is a promising technique and has some advantages over the classical methods based on averaging historical data. First, the determining parameters are highly scalable for newly drilled wells as the main input parameters are dimensionless variables derived from reservoir properties and well completions. Secondly, this algorithm explores not only the production data but also reservoir properties and completion data to capitalize on the advancing techniques.