Low oil recovery which is very predominant in shale oil reservoirs has stimulated petroleum engineers to investigate the applications of enhanced oil recovery methods in these formations. One such application is the injection of gases into the formation to stimulate increased oil recovery. In many gas flooding projects performed in the field, the miscibility of the gas injected is usually the most desired displacement mechanism, and carbon dioxide (CO2) gas has been recognized to be the best performing gas for injection due to its ability to be miscible with oil in the reservoir at low pressures compared to other gases such as nitrogen. This minimum miscibility pressure (MMP) is of very crucial importance because it is the primary limiting factor in the feasibility of a miscible gas flooding project. However, there are other limiting factors such as cost and availability and, in these instances, nitrogen (N2) and lean gas are the more preferred candidate as opposed to carbon dioxide gas. Mixing carbon dioxide gas with lean gas or with nitrogen in a required ratio can allow us to design an injection gas that will be suitable enough to satisfy both the availability and cost constraints and at the same time allow us to achieve a reachable and reasonable miscibility pressure. The objective of this paper is to investigate the effect of mixing nitrogen gas and carbon dioxide gas in a 50:50 ratio on oil recovery in tight oil formations. The experiment was performed with controlled constraints such as the same core sample, same crude oil and same core cleaning and saturation process which was repeated for each trial. The oil used was live oil from Eagle ford formation, and the gases used were nitrogen (99.9% purity), carbon dioxide and a mixture of nitrogen and carbon dioxide in a 50:50 ratio. The injection pressure ranged from 1000 to 5000 psi with pressure increments of 1000 psi, and the same flooding time was 6 h. The potential of the N2, CO2 and N2–CO2 mixture for improving oil recovery was assessed along with the breakthrough time. The results showed that CO2 gas had the highest recovery followed by the N2–CO2 mixture and N2 gas had the lowest recovery. The gas breakthrough time results showed that the N2–CO2 mixture had the longest breakthrough time, N2 had the shortest breakthrough time, and CO2 had a significantly longer breakthrough time than pure N2 gas. The RF increased with increasing pressure, but the gas breakthrough time decreased with increasing pressure. However, the incremental RF decreased in all three cases when the injection pressure was above 3000 psi.

Modern data analytic techniques, statistical and machine-learning algorithms have received widespread applications for solving oil and gas problems. As we face problems of parent–child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We accomplish this using a well-formulated spatio-temporal (ST) model. In this paper, we present a multi-basin study of production performance evaluation and applications of ST models for oil and gas data. We analyzed dataset from 10,077 horizontal wells from 2008 to 2019 in five unconventional formations in the USA: Bakken, Marcellus, Eagleford, Wolfcamp, and Bone Spring formations. We evaluated well production performance and performance of new completions over time. Results show increased productivity of oil and gas since 2008. Also, the Bakken wells performed better for the counties evaluated. We present two methods for fitting spatio-temporal models: fixed rank kriging and ST generalized additive models using thin plate and cubic regression splines as basis functions in the spline-based smooths. Results show a significant effect on production by the smooth term, accounting for between 60 and 95% of the variability in the six-month production. Overall, we saw a better production response to completions for the gas formations compared to oil-rich plays. The results highlight the benefits of spatio-temporal models in production prediction as it implicitly accounts for geology and technological changes with time.

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