Validation of analytical model and identification of salt effect on wellbore temperature in underbalanced drilling

Understanding the behavior of the borehole temperature recovery process, which influences drilling operations, requires an adequate estimation of fluid temperature. The presence of salt in a saline formation changes the composition of the annular fluid and has a significant impact on the fluid temperature distribution during drilling operations. As a result, while drilling a saline formation, it is vital to examine the key parameter that determines an accurate estimate of fluid temperature. Using python software and statistical quantitative methods, this study proposes a simplified user-friendly computational system that analyzes the drilling fluid systems performance evaluation and selection optimization.The fluid temperature distribution of X Field in China was analyzed using Shan mathematical model as a base model. When compared to MWD data from the field, the model predicted the temperature distribution of the field with less than 10% error. An adjustment factor was introduced to the base model to accommodate for changes in annular fluid composition while drilling a saline formation. The findings show that salt concentration has an impact on fluid temperature distribution during drilling. The fluid temperature at the wellbore condition changes by at least 7% with both high and low adjustment factors. Because the salt in the formation inflow dissolves in the drilling fluid near the annulus, the rheology of the fluid combination changes.

Metagenomic Investigation of a Low Diversity, High Salinity Offshore Oil Reservoir

Oil reservoirs can represent extreme environments for microbial life due to low water availability, high salinity, high pressure and naturally occurring radionuclides. This study investigated the microbiome of saline formation water samples from a Gulf of Mexico oil reservoir. Metagenomic analysis and associated anaerobic enrichment cultures enabled investigations into metabolic potential for microbial activity and persistence in this environment given its high salinity (4.5%) and low nutrient availability. Preliminary 16S rRNA gene amplicon sequencing revealed very low microbial diversity. Accordingly, deep shotgun sequencing resulted in nine metagenome-assembled genomes (MAGs), including members of novel lineages QPJE01 (genus level) within the Halanaerobiaceae, and BM520 (family level) within the Bacteroidales. Genomes of the nine organisms included respiratory pathways such as nitrate reduction (in Arhodomonas, Flexistipes, Geotoga and Marinobacter MAGs) and thiosulfate reduction (in Arhodomonas, Flexistipes and Geotoga MAGs). Genomic evidence for adaptation to high salinity, withstanding radioactivity, and metal acquisition was also observed in different MAGs, possibly explaining their occurrence in this extreme habitat. Other metabolic features included the potential for quorum sensing and biofilm formation, and genes for forming endospores in some cases. Understanding the microbiomes of deep biosphere environments sheds light on the capabilities of uncultivated subsurface microorganisms and their potential roles in subsurface settings, including during oil recovery operations.

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

Investigation of the structure of borehole zones at various stages of development of saline reservoirs with textural wettability

A laboratory and numerical study of the mechanisms that affect the structure and physical properties of the borehole zone of a production well at various stages of development of a saline formation with textural wettability was conducted. It is shown that for the object of study is characterized by the formation of complex borehole vicinity of the structure and dynamic change of properties which define the specific geotechnical effects: desalinization; pinched adscititious water in the pore space; the decompression and the strain on the washed areas; precipitation of solid salt sediment at the moment of breaking through the highly mineralized front of the injected water. The synergetic effect of these effects leads to the formation of complex, including non-monotonic, permeability distribution profiles in the near-well zone.