Robust optimization of CO2 sequestration through a water alternating gas process under geological uncertainties in Cuu Long Basin, Vietnam

Ren

et al. 2021

Preprint

Carbon dioxide (CO2) capture and storage (CCS) is presented as an alternative measure and promising approach to mitigate the large-scale anthropogenic CO2 emission into the atmosphere. In this context, CO2 sequestration into depleted oil reservoirs is a practical approach as it boosts the oil recovery and facilitates the permanent storing of CO2 into the candidate sites. However, the estimation of CO2 storage capacity in subsurfaces is a challenge to kick-start CCS worldwide. Thus, this paper proposes an integrated static and dynamic modeling framework to tackle the challenge of CO2 storage capacity in a clastic reservoir, S1A filed, Masila basin, Yemen. To achieve this work's ultimate goal, the geostatistical modeling was integrated with open-source code (MRST-CO2lab) for reducing the uncertainty assessment of CO2 storage capacity. Also, there is a significant difference between static and dynamic CO2 storage capacity. The static CO2 storage capacity varies from 4.54 to 81.98 million tons, while the dynamic CO2 simulation is estimated from 4.95 to 17.92 million tons. Based on the geological uncertainty assessment of three ranked realizations (P10, P50, P90), our work was found that the upper Qinshn sequence could store 15.64 Million tons without leakage. This result demonstrates that the potential of CO2 utilization is not only in this specific reservoir, but the further CO2 storage for the other clastics reservoirs is promising in the Masila Basin, Yemen.

Section: Introductionmentioning

confidence: 99%

Integrated static modelling and dynamic simulation framework for CO2 storage capacity in upper Qishn Clastics, S1A reservoir, Yemen

AlRassas

Ren

et al. 2021

Preprint

“…Moreover, Ahmadi et al 35 applied ANN for the prediction of the CO 2 properties in carbon capture and sequestration operations. Besides, ANN models were used for the evaluation performance of the WAG process in CO 2 -EOR and sequestration projects 36 , 37 .…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Sugai

Sasaki

2020

Sci Rep

Self Cite

100

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

“…Also, ANN was utilized to generate a predictive model to define condensate-to-gas ratio and dew point pressure in retrograded condensate gas reservoirs (Ahmadi et al 2014b;Ahmadi and Ebadi 2014). Other diverse applications in production technology are: (1) machine learning-based ANN to predict the bottom hole pressure in multiphase flow in vertical oil production (Ahmadi and Chen 2019); (2) a robust intelligent model to monitor the performance of chemical flooding in oil reservoirs (Ahmadi 2015); (3) performance monitoring of CO 2 -foam flooding for EOR and CO 2 storage (Moosavi et al 2019); and (4) improving CO 2 water-alternating-gas (WAG) combining sequential Gaussian simulation (SGS), co-kriging and ANN to optimize both CO 2 storage and enhance oil recovery (Vo Thanh et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Microbe-induced fluid viscosity variation: field-scale simulation, sensitivity and geological uncertainty

Ansah

J Petrol Explor Prod Technol

Sugai

et al. 2020

Self Cite

This study is intended to expand the scope of microbial enhanced oil recovery (MEOR) simulation studies from 1D to field scale focussing on fluid viscosity variation and heterogeneity that lacks in most MEOR studies. Hence, we developed a model that incorporates: (1) reservoir simulation of microbe-induced oil viscosity reduction and (2) field-scale simulation and robust geological uncertainty workflow considering the influence of well placement. Sequential Gaussian simulation, co-kriging and artificial neural network were used for the petrophysical modelling prior to field-scale modelling. As per this study, the water viscosity increased from 0.5 to 1.72 cP after the microbe growth and increased biomass/biofilm. Also, we investigated the effect of the various component compositions and reaction frequencies on the oil viscosity and possibly oil recovery. For instance, the fraction of the initial CO 2 in the oil phase (originally in the reservoir) was varied from 0.000148 to 0.005 to promote the reactions, and more light components were produced. It can be observed that the viscosity of oil reduced considerably after 90 days of MEOR operation from an initial 7.1-7.07 cP and 6.40 cP, respectively. Also, assessing the pre-and post-MEOR oil production rate, we witnessed two main typical MEOR field responses: sweeping effect and radial colonization occurring at the start and tail end of the MEOR process, respectively. MEOR oil recovery factors varied from 28.2 to 44.9% OOIP for the various 200 realizations. Since the well placement was the same for all realizations, the difference in the permeability distribution amongst the realizations affected the microbes' transport and subsequent interaction with nutrient during injection and transport. 1 3 R c Growth and death rate of microorganism (cells/ m 3 s) R m Consumption rate of nutrients by microorganism (kg/m 3 s) R p Production rate of metabolite by microorganism (1/s) W c Cell concentration (cells/m 3 ) W m Nutrients' concentration (kg/m 3 ) W p Metabolites' concentration Φ o Flow potential of oil Φ w Flow potential of water q op Production rate of oil (kg/m 3 s) q wi Injection rate of water (kg/m 3 s) q wp Production rate of water (kg/m 3 s) GRNN Generalized regression neural network MLR Multiple linear regression ANN Artificial neural network LSSVM Least square support vector machine