Improving Water-Based Drilling Mud Performance Using Biopolymer Gum: Integrating Experimental and Machine Learning Techniques.

Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.

A Review of Advanced Molecular Engineering Approaches to Enhance the Thermostability of Enzyme Breakers: From Prospective of Upstream Oil and Gas Industry.

During the fracture stimulation of oil and gas wells, fracturing fluids are used to create fractures and transport the proppant into the fractured reservoirs. The fracturing fluid viscosity is responsible for proppant suspension, the viscosity can be increased through the incorporation of guar polymer and cross-linkers. After the fracturing operation, the fluid viscosity is decreased by breakers for efficient oil and gas recovery. Different types of enzyme breakers have been engineered and employed to reduce the fracturing fluid's viscosity, but thermal stability remains the major constraint for the use of enzymes. The latest enzyme engineering approaches such as direct evolution and rational design, have great potential to increase the enzyme breakers' thermostability against high temperatures of reservoirs. In this review article, we have reviewed recently advanced enzyme molecular engineering technologies and how these strategies could be used to enhance the thermostability of enzyme breakers in the upstream oil and gas industry.

Reduction of Breakdown Pressure by Filter Cake Removal Using Thermochemical Fluids and Solvents: Experimental and Numerical Studies.

The process of well cleanup involves the removal of an impermeable layer of filter cake from the face of the formation. The inefficient removal of the filter cake imposes difficulty on fracturing operations. Filter cake's impermeable features increase the required pressure to fracture the formation. In this study, a novel method is introduced to reduce the required breakdown pressure to fracture the formation containing the water-based drilling fluid filter cake. The breakdown pressure was tested for five samples of similar properties using different solutions. A simulated borehole was drilled in the core samples. An impermeable filter cake using barite-weighted drilling fluid was built on the face of the drilled hole of each sample. The breakdown pressure for the virgin sample without damage (filter cake) was 6.9 MPa. The breakdown pressure increased to 26.7 MPa after the formation of an impermeable filter cake. Partial removal of filter cake by chelating agent reduced the breakdown pressure to 17.9 MPa. Complete dissolution of the filter cake with chelating agents resulted in the breakdown pressure approximately equivalent to the virgin rock breakdown pressure, i.e., 6.8 MPa. The combined thermochemical and chelating agent solution removed the filter cake and reduced the breakdown pressure to 3.8 MPa. Post-treatment analysis was carried out using nuclear magnetic resonance (NMR) and scratch test. NMR showed the pore size redistributions with good communication between different pores after the thermochemical removal of filter cake. At the same time, there was no communication between the different pores due to permeability impairment after filter cake formation. The diffusion coupling through NMR scans confirmed the higher interconnectivity between different pores systems after the combined thermochemical and chelating agent treatment. Compressive strength was measured from the scratch test, confirming that filter cake formation caused added strength to the rock that impacts the rock breakdown pressure. The average compressive strength of the original specimen was 44.5 MPa that increased to 73.5 MPa after the formation of filter cake. When the filter cake was partially removed, the strength was reduced to 61.7 MPa. Complete removal with chelating agents removed the extra strength that was added due to the filter cake presence. Thermochemical and chelating agents resulted in a significantly lower compressive strength of 25.3 MPa. A numerical model was created to observe the reduction in breakdown pressure due to the thermochemical treatment of the filter cake. The result presented in this study showed the engineering applications of thermochemical treatment for filter cake removal.

A case of life-threatening retinoic acid syndrome and review of literature.

All-trans-retinoic acid represents a major progress that has made acute promyelocytic leukemia the most curable subtype of acute myeloid leukemia in adults. Although all-trans-retinoic acid is usually well tolerated, some patients develop the retinoic acid syndrome, characterized by unexplained fever, weight gain, respiratory distress, interstitial pulmonary infiltrates, pleural and pericardial effusions, episodic hypotension, and acute renal failure. Further studies of growth factor expression and modulation of adhesion molecules are warranted to provide further insights into the pathogenesis of the syndrome and may lead to its prevention.

The use of FreeStyle libre improves glycaemic control along with reducing diabetes burden and hospital admissions in a socially deprived Northwest English population.

Aim: This retrospective study aimed to use mixed (qualitative and quantitative) methods to evaluate the role of FSL in reducing hospital admissions due to all causes, HbA1c, and reported hypoglycaemic episodes in people with diabetes living in a socially deprived region of Northwest England. Methods: Data were collected retrospectively from previous consultations, which coincided with the 6th -week, 6th -month and annual review including blood tests, hospital admissions due to any cause and reported hypoglycaemia. Also, FSL assessment and satisfaction semi-structured questionnaire was done to assess the impact of FSL on diabetes management and quality of life. Mixed-effects models were used to assess glycaemic control and reductions in hospital admissions and reported hypoglycaemic episodes. Results: Just 127 patients met the inclusion criteria. A multivariate linear mixed model method that analyses HbA1c data longitudinally revealed mean differences (mmol/mol) between baseline and post-FSL measurements, estimated by restricted maximum likelihood method (REML) of 9.64 (six weeks), 7.68 (six months) and 7.58 (annual review); all with a corresponding p-value of < 0.0001. For DKA patients, the bootstrap method revealed a significant reduction in mean HbA1c of 25.5, 95% confidence interval (CI) [8.8, 42.6] mmol/mol. It is demonstrated that FSL use for one year resulted in 59% reduction in hospital admissions and 46% reduction in reported hypoglycaemic episodes. Conclusion: The use of FSL resulted in statistically significant reductions in hospital admissions, HbA1c and reported hypoglycaemic episodes among diabetics in a socially deprived Northwest region of England. These outcomes show a direct association with a higher questionnaire score. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01424-4.

Fractured Geothermal Reservoir Using CO2 as Geofluid: Numerical Analysis and Machine Learning Modeling.

The effect of natural fractures, their orientation, and their interaction with hydraulic fractures on the extraction of heat and the extension of injection fluid are fully examined. A fully coupled and dynamic thermo-hydro-mechanical (THM) model is utilized to examine the behavior of a fractured geothermal reservoir with supercritical CO2 as a geofluid. The interaction between natural fracture and hydraulic fracture, as well as the type and location of geofluids, influences the production temperature, thermal strain, mechanical strains, and effective stress in rock/fractures in the reservoir. A mathematical model is developed by using the fully connected neural network (FCN) model to establish a mathematical relationship between the reservoir parameters and the temperature. The response surface methodology is applied for qualitative numerical experimentation. It is found that the developed FCN model can be utilized to forecast the temporal variation of temperature in the production well to a desired level using FCN. Therefore, the numerical simulations developed with the FCN method can be useful tools to investigate the temperature evolution with higher accuracy.

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools.

Rheological models are usually used to predict foamed fluid viscosity; however, obtaining the model constants under various conditions is challenging. Hence, this paper investigated the effect of different variables on foam rheology, such as shear rate, temperature, pressure, surfactant types, gas phase, and salinity, using a high-pressure high-temperature foam rheometer. Power-law, Bingham plastic, and Casson fluid models fit the experimental data well. Therefore, the data were fed to different machine learning techniques to evaluate the rheological model constants with different features. In this study, seven different machine learning techniques have been applied to predict the rheological models' constants, including decision tree, random forest, XGBoost (XGB), adaptive gradient boosting, gradient boosting, support vector regression, and voting regression. We evaluated the performance of our machine learning models using the coefficient of determination (R2), cross-plots, root-mean-square error, and average absolute percentage error. Based on the prediction outcomes, the XGB model outperformed the other ML models. The XGB model exhibited remarkably low error rates, achieving a prediction accuracy of 95% under ideal conditions. Furthermore, our prediction results demonstrated that the Casson model accurately captured the rheological behavior of the foam. Additionally, we used Pearson's correlation coefficients to assess the significance of various properties in relation to the constants within the rheological models. It is evident that the XGB model makes predictions with nearly all features contributing significantly, while other machine learning techniques rely more heavily on specific features over others. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serves as a quick assessment tool.

Manganese doped tailored cobalt sulfide as an accelerated catalyst for oxygen evolution reaction.

Developing an efficient, robust, and noble metal-free electrocatalyst that can catalyse oxygen evolution reactions (OER) remains a significant challenge. CoS2, a representative of pyrite form transition metal dichalcogenides, has recently been identified as an economical catalyst. Here, an incredibly quick and scalable technique for novel catalysts synthesized with the use of the microwave method was introduced. Manganese-doped cobalt sulphide (Mn-CoS2) showed outstanding OER with a very low overpotential of 227 mV at 10 mA cm-2. Exposure of surface atoms resulted in high electrochemical activity, where the defects facilitated charge and mass transfer along the nanostructure, allowing surface dependent electrochemical reactions to be performed more efficiently. The electronic properties of pristine and transition-metal-doped CoS2 structures were also investigated using density functional theory (DFT). To better understand transition metal's dependent impact on crystal structure, orbital electronic participation, charge density, and charge transformation in both pristine and Mn-dopedCoS2 frameworks were calculated and analysized. Our synthesis approach is primarily commercial and extensible, overcoming synthesis challenge of transition metal sulphide nanostructures with prime quality and implying a potential for commercial uses.

Machine learning accelerated approach to infer nuclear magnetic resonance porosity for a middle eastern carbonate reservoir.

Carbonate rocks present a complicated pore system owing to the existence of intra-particle and interparticle porosities. Therefore, characterization of carbonate rocks using petrophysical data is a challenging task. Conventional neutron, sonic, and neutron-density porosities are proven to be less accurate as compared to the NMR porosity. This study aims to predict the NMR porosity by implementing three different machine learning (ML) algorithms using conventional well logs including neutron-porosity, sonic, resistivity, gamma ray, and photoelectric factor. Data, comprising 3500 data points, was acquired from a vast carbonate petroleum reservoir in the Middle East. The input parameters were selected based on their relative importance with respect to output parameter. Three ML techniques such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and functional network (FN) were implemented for the development of prediction models. The model's accuracy was evaluated by correlation coefficient (R), root mean square error (RMSE), and average absolute percentage error (AAPE). The results demonstrated that all three prediction models are reliable and consistent exhibiting low errors and high 'R' values for both training and testing prediction when related to actual dataset. However, the performance of ANN model was better as compared to other two studied ML techniques based on minimum AAPE and RMSE errors (5.12 and 0.39) and highest R (0.95) for testing and validation outcome. The AAPE and RMSE for the testing and validation results were found to be 5.38 and 0.41 for ANFIS and 6.06 and 0.48 for FN model, respectively. The ANFIS and FN models exhibited 'R' 0.937 and 0.942, for testing and validation dataset, respectively. Based on testing and validation results, ANFIS and FN models have been ranked second and third after ANN. Further, optimized ANN and FN models were used to extract explicit correlations to compute the NMR porosity. Hence, this study reveals the successful applications of ML techniques for the accurate prediction of NMR porosity.

Predicting wettability of mineral/CO2/brine systems via data-driven machine learning modeling: Implications for carbon geo-sequestration.

Effectively storing carbon dioxide (CO2) in geological formations synergizes with algal-based removal technology, enhancing carbon capture efficiency, leveraging biological processes for sustainable, long-term sequestration while aiding ecosystem restoration. On the other hand, geological carbon storage effectiveness depends on the interactions and wettability of rock, CO2, and brine. Rock wettability during storage determines the CO2/brine distribution, maximum storage capacity, and trapping potential. Due to the high CO2 reactivity and damage risk, an experimental assessment of the CO2 wettability on storage/caprocks is challenging. Data-driven machine learning (ML) models provide an efficient and less strenuous alternative, enabling research at geological storage conditions that are impossible or hazardous to achieve in the laboratory. This study used robust ML models, including fully connected feedforward neural networks (FCFNNs), extreme gradient boosting, k-nearest neighbors, decision trees, adaptive boosting, and random forest, to model the wettability of the CO2/brine and rock minerals (quartz and mica) in a ternary system under varying conditions. Exploratory data analysis methods were used to examine the experimental data. The GridSearchCV and Kfold cross-validation approaches were implemented to augment the performance abilities of the ML models. In addition, sensitivity plots were generated to study the influence of individual parameters on the model performance. The results indicated that the applied ML models accurately predicted the wettability behavior of the mineral/CO2/brine system under various operating conditions, where FCFNN performed better than other ML techniques with an R2 above 0.98 and an error of less than 3%.

High-throughput genotyping in osteosarcoma identifies multiple mutations in phosphoinositide-3-kinase and other oncogenes.

BACKGROUND: The identification of new genes that are mutated in osteosarcomas is critical to developing a better understanding of the molecular pathogenesis of this disease and discovering new targets for therapeutic development. METHODS: The authors identified somatic nonsynonymous coding mutations in oncogenes associated with human cancers and hotspot mutations from tumor suppressor genes that were either well described in the literature or observed multiple times in human cancer sequencing efforts. Then, 961 mutations in 89 genes were systematically characterized across 98 osteosarcoma tumor samples and cell lines. All identified mutations were replicated on an independent platform using homogeneous mass extend matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. RESULTS: In total, 14 mutations were identified in at least 1 osteosarcoma tumor sample or cell line. Some of the genetic changes identified were in tumor suppressor genes previously identified as altered in osteosarcoma: p53 (arginine→histidine at codon 273 [R273H], R→cysteine at codon 723 [R273C], and tyrosine→C at codon 163 [Y163C]) and retinoblastoma 1 (RB1) (glutamic acid→* at codon 137 [E137*]). Notably, multiple mutations were identified in phosphoinositide-3-kinase (PI3K), catalytic, alpha polypeptide (PIK3CA) (H1047R, E→lysine at codon 545 [E545K], and H→proline at codon 701 [H701P]) that were not observed previously in osteosarcoma. In addition, mutations in v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) (glycine→serine at codon 12 [G12S]); cubilin (CUBN) (isolucine→valine at codon 3189 [I3189V]; observed in 2 separate tumor samples); cadherin 1, type 1, epithelial (CDH1) (alanine→threonine at codon 617 [A617T]; observed in 2 separate tumor samples); catenin (cadherin-associated protein), beta 1, 88 kDa (CTNNB1) (asparagine→S at codon 287 [N287S]); and fibrous sheath CABYR binding protein (FSCB) (S→leucine at codon 775 [S775L]) were observed. CONCLUSIONS: In this largest mutational profiling of osteosarcoma to date, the authors identified for the first time several mutations involving the PI3K pathway, adding osteosarcoma to the growing list of malignancies with PI3K mutations. In addition, they initiated a mutational map detailing DNA sequence changes across a variety of osteosarcoma subtypes and offered new candidates for therapeutic targeting.

Treatment of multidrug resistant advanced alveolar soft part sarcoma with sunitinib.

Sunitinib is a tyrosine kinase/angiogenesis inhibitor with proven efficacy in gastrointestinal stromal tumor and advanced renal cell carcinoma. We are presenting the case report of a patient with aggressive alveolar soft part sarcoma with lung and bone metastases, who had failed multiple chemotherapy regimens showing significant response to sunitinib. There was not only complete regression of the primary tumor, stabilization of his bone metastases and significant improvement in the quality of life. Our report shows that sunitinib has the capability of playing a pivotal role in the management of non-gastrointestinal stromal tumors like alveolar soft part sarcoma. Further research and trials must be encouraged over the use of this drug as it is most definitely promising.

Metastatic renal extraskeletal Ewing sarcoma in complete remission for the last 8 years.

Primary renal extraskeletal Ewing sarcoma (EES) is rare but well known to be aggressive, less responsive to the treatment, and has early predilection for metastases. Metastases at the time of diagnosis to the lungs or bones are associated with poor outcome. We present a case of primary renal EES in 57-year-old woman with multiple metastases to the lungs at the time of diagnosis with complete remission of the disease for the last 8 years following multimodality treatment Multidisciplinary approach for the management of EES has definitely improved the quality of life and the survival of the patients.

A case report of complete remission of pulmonary metastases from epithelioid sarcoma to navelbine chemotherapy.

Management of soft tissue sarcomas can be very challenging because they have a high rate of metastasis, especially to the lungs, and respond very poorly to the currently available chemotherapeutic drugs. We present a case of epithelioid sarcoma in which complete remission of pulmonary metastases was observed after treatment with a single agent, navelbine, a vinca alkaloid, and a potential therapeutic agent. The patient has been persistently free of metastases for 4 years since treatment with navelbine. Further studies are warranted to establish the role of navelbine for the treatment of soft tissue sarcoma and their metastases.

A Review of Enzyme-Induced Calcium Carbonate Precipitation Applicability in the Oil and Gas Industry.

Enzyme-induced calcium carbonate precipitation (EICP) techniques are used in several disciplines and for a wide range of applications. In the oil and gas industry, EICP is a relatively new technique and is actively used for enhanced oil recovery applications, removal of undesired chemicals and generating desired chemicals in situ, and plugging of fractures, lost circulation, and sand consolidation. Many oil- and gas-bearing formations encounter the problem of the flow of sand grains into the wellbore along with the reservoir fluids. This study offers a detailed review of sand consolidation using EICP to solve and prevent sand production issues in oil and gas wells. Interest in bio-cementation techniques has gained a sharp increase recently due to their sustainable and environmentally friendly nature. An overview of the factors affecting the EICP technique is discussed with an emphasis on the in situ reactions, leading to sand consolidation. Furthermore, this study provides a guideline to assess sand consolidation performance and the applicability of EICP to mitigate sand production issues in oil and gas wells.

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types.

Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σt) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σt), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.