Geomechanics contribution to CO2 storage containment and trapping mechanisms in tight sandstone complexes: A case study on Mae Moh Basin

Recognized as a not-an-option approach to mitigate the climate crisis, carbon dioxide capture and storage (CCS) has a potential as much as gigaton of CO2 to sequestrate permanently and securely. Recent attention has been paid to store highly concentrated point-source CO2 into saline formation, of which Thailand considers one onshore case in the north located in Lampang - the Mae Moh coal-fired power plant matched with its own coal mine of Mae Moh Basin. Despite a large basin and short transport route from the source, target sandstone reservoir buried at deeper than 1000 m is of tight nature and limited data, while question on storing possibility has thereafter risen. The current study is thus aimed to examine the influence of reservoir geomechanics on CO2 storage containment and trapping mechanisms, with co-contributions from geochemistry and reservoir heterogeneity, using reservoir simulator - CMG-GEM. With the injection rate designed for 30-year injection, reservoir pressure build-ups were ∼77 % of fracture pressure but increased to ∼80 % when geomechanics excluded. Such pressure responses imply that storage security is associated with the geomechanics. Dominated by viscous force, CO2 plume migrated more laterally while geomechanics clearly contributed to lesser migration due to reservoir rock strength constraint. Reservoir geomechanics contributed to less plume traveling into more constrained spaces while leakage was secured, highlighting a significant and neglected influence of geomechanical factor. Spatiotemporal development of CO2 plume also confirms the geomechanics-dominant storage containment. Reservoir geomechanics as attributed to its respective reservoir fluid pressure controls development of trapping mechanisms, especially into residual and solubility traps. More secured storage containment after the injection was found with higher pressure, while less development into solubility trap was observed with lower pressure. The findings reveal the possibility of CO2 storage in tight sandstone formations, where geomechanics govern greatly the plume migration and the development of trapping mechanisms.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

A novel framework of reliability analysis was developed in this study to consider the uncertainty of geomaterials and geological conditions by combining the reduced-order model (ROM), reliability analysis, and numerical model. The reliability method was used to determine the reliability index using the simplicial homology global optimization (SHGO) based on the ROM. The developed method was verified and illustrated using three numerical examples and a simple slope. The limit state curve in all three numerical examples was in excellent agreement with the actual curve. The reliability index and failure probability were also in excellent agreement with those of the actual limit state function using the first-order reliability method (FORM) and Monte Carlo simulation, respectively, indicating that the ROM method can present the limit state function well. The results showed that the developed method is feasible and effective for reliability analysis of geotechnical and geological engineering problems with a complex, nonlinear, and implicit limit state function. Furthermore, the developed method is effective, efficient, and accurate for reliability analysis. It provides an excellent way to approximate the limit state function to avoid the time-consuming numerical model in a practical engineering system.

Data for evaluation of the onshore Cretaceous Zululand Basin in South Africa for geological CO2 storage

The world has set the goal of reducing CO2 emissions from burning fossil fuels by using carbon capture and storage (CCS) as one of the major solutions. A sudden and complete switch from fossil fuels to renewable resources cannot be achieved immediately. Therefore, CCS remains an essential techniques to reduce CO2. In this work, the 180 - 65 Ma old onshore part of the Zululand Basin in KwaZulu-Natal in South Africa was investigated for geological CO2 sequestration. A total of 160 core samples of sandstone, conglomerate, tuff, rhyolite, breccia, and siltstone were taken from NZA, ZA, ZB, and ZC drill cores. The wells were drilled in the 1960s by the South African Petroleum and Gas Corporation Company for hydrocarbon exploration. In order to examine the basin suitability for CO2 storage, porosity and permeability, mineralogy, geochemistry, geomechanical properties, and H2O-CO2-rock interactions were investigated using geological core logging, spectral scanning, petrography, X-ray diffraction (XRD), X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry, uniaxial compressive stress, and scanning electron microscopy. The basin comprises clastic sedimentary rocks, pyroclastic deposits and carbonates from the Makatini, Mzinene and St. Lucia formations. Aptian and Cenomanian sandstones are identified as CO2 reservoirs, and the siltstone above is considered capstone. The sandstone comprises on average 34.45 wt% quartz, 32.91 wt% clays, 29.53 wt% feldspars, 4.44 wt% carbonates, 3.10 wt% Fe-oxides, 2.40 wt% micas, and 2.00 wt% organic materials as per XRD data, also contains trace amounts of sulphides and sulphates. Geochemical XRF data for sandstone are 29.72 - 62.51 wt% SiO2, 6.95 - 13.44 wt% Al2O3, 3.06 - 48.81 wt%, 1.90 - 4.51 wt% MgO, 1.04 - 2.19 wt% K2O, 1.00 - 3.67 Na2O wt%. The content of TiO2, Cr2O3 and P2O5 is below 0.01 wt% each. Siltstone has similar mineralogy and geochemistry as sandstone, but high clay content, fine-grained, impervious, with porosity <5%. The sandstone and siltstone are geomechanically soft and recorded 15 MPa on the Enerpac P141 device. CO2-H2O-rock interaction experiments performed at 100 °C and 100 bar using autoclaves showed that sandstone and siltstone react with scCO2.

A modelling study to evaluate the effect of impure CO2 on reservoir performance in a sandstone saline aquifer

Carbon capture and storage (CCS) is expected to play a key role in meeting greenhouse gas emissions reduction targets. In the UK Southern North Sea, the Bunter Sandstone formation (BSF) has been identified as a potential reservoir which can store very large amounts of CO2. The formation has fairly good porosity and permeability and is sealed with both effective caprock and base rock, making CO2 storage feasible at industrial scale. However, when CO2 is captured, it typically contains impurities, which may shift the boundaries of the CO2 phase diagram, implying that higher costs will be needed for storage operations. In this study, we modelled the effect of CO2 and impurities (NO2, SO2, H2S) on the reservoir performance of the BSF. The injection of CO2 at constant rate and pressure using a single horizontal well injection strategy was simulated for up to 30 years, as well as an additional 30 years of monitoring. The results suggest that impurities in the CO2 stream affect injectivity differently, but the effects are usually encountered during early stages of injection into the BSF and may not necessarily affect cumulative injection over an extended period. It was also found that porosity of the storage site is the most important factor controlling the limits on injection. The simulations also suggest that CO2 remains secured within the reservoir for 30 years after injection is completed, indicating that no post-injection leakage is anticipated.

Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the ‘degree of reliability (degree of stability-instability contingency)’ for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making.