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Formation pressure estimation of shale gas wells is one of the key factors affecting production prediction of gas wells, and diagnostic fracture injection test analysis is an important means to accurately obtain formation pressure parameters. In this paper, through the establishment of shale gas well diagnostic fracture injection test model, taking ChangNing shale gas field as an example, the actual test data of pre-production gas wells are fitted. Then analysis the change law of pressure transient curves and the model fitting results with conventional analysis method comparing with the results - G function diagnosis curve method to explain, optimizing fracture closure point selection mode, this paper proposes a new method to determine fracture closure point: when G derivative curve begins to deviate from the initial linear trend, correct selection of closed point, to obtain accurate formation pressure coefficient, formation pore pressure of reservoir parameters such as reservoir. At the same time, the improved method is used to evaluate the reservoir parameters of the actual gas wells in ChangNing, providing theoretical guidance for the subsequent fracturing construction and production.

The reservoir characteristics of tight sandstone gas reservoirs are characterized by low porosity and low permeability, which brings difficulties to the conventional pressure recovery well test in oil fields. The lower porosity and permeability greatly extend the test time. Radial flow was not detected when the oil well was shut down for more than one month. The well test curve showed strong multi-solution characteristics and could not obtain accurate formation parameters. In this paper, based on the characteristics of tight gas reservoirs, the well test interpretation method (PDA method) is applied to the application of production data. A set of explanations for using the production data to explain tight gas wells and carrying out capacity evaluation and prediction are proposed. The interpretation of the production data of 1 is compared with the interpretation results of the pressure recovery test of the well. The two methods have good consistency, and the multiple interpretation methods can better reduce the multi-solution of the well test interpretation, and verify the PDA. The feasibility of the method applied on tight gas wells, and finally the correctness of the method was verified by the capacity prediction method.

Neonicotinoids are a popular and widely-used class of insecticides whose heavy usage rates and purported negative impacts on bees and other beneficial insects has led to questions about their mobility and accumulation in the environment. Neonicotinoid compounds are currently registered for over 140 different crop uses in the United States, with commercial growers continuing to rely heavily on neonicotinoid insecticides for the control of key insect pests through a combination of in-ground and foliar applications. In 2008, the Wisconsin Department of Agriculture, Trade and Consumer Protection (DATCP) began testing for neonicotinoids in groundwater test wells in the state, reporting detections of one or more neonicotinoids in dozens of shallow groundwater test wells. In 2011, similar detection levels were confirmed in several high-capacity overhead center-pivot irrigation systems in central Wisconsin. The current study was initiated to investigate the spatial extent and magnitude of neonicotinoid contamination in groundwater in and around areas of irrigated commercial agriculture in central Wisconsin. From 2013-2015 a total of 317 samples were collected from 91 unique high-capacity irrigation wells and tested for the presence of thiamethoxam (TMX), a neonicotinoid, using enzyme-linked immunosorbent assays. 67% of all samples were positive for TMX at a concentration above the analytical limit of quantification (0.05 μg/L) and 78% of all wells tested positive at least once. Mean detection was 0.28 μg/L, with a maximum detection of 1.67 μg/L. Five wells had at least one detection exceeding 1.00 μg/L. Furthermore, an analysis of the spatial structure of these well detects suggests that contamination profiles vary across the landscape, with differences in mean detection levels observed from landscape (25 km), to farm (5 km), to individual well (500 m) scales. We also provide an update of DATCP's neonicotinoid monitoring in Wisconsin's shallow groundwater test wells and private potable wells for the years 2011-2017.

Serological detection of antibodies to SARS-CoV-2 is essential for establishing rates of seroconversion in populations, and for seeking evidence for a level of antibody that may be protective against COVID-19 disease. Several high-performance commercial tests have been described, but these require centralised laboratory facilities that are comparatively expensive, and therefore not available universally. Red cell agglutination tests do not require special equipment, are read by eye, have short development times, low cost and can be applied at the Point of Care. Here we describe a quantitative Haemagglutination test (HAT) for the detection of antibodies to the receptor binding domain of the SARS-CoV-2 spike protein. The HAT has a sensitivity of 90% and specificity of 99% for detection of antibodies after a PCR diagnosed infection. We will supply aliquots of the test reagent sufficient for ten thousand test wells free of charge to qualified research groups anywhere in the world. Serological detection of antibodies against SARS-CoV-2 can help establish rates of seroconversion. Here the authors develop a red cell agglutination test to detect antibodies against the receptor binding domain for distribution free of charge to qualified research groups.

The article analyzes the effect of the limiting shear gradient of viscoplastic oil on the results of interpretation of well testing by the pressure recovery curve (PRC) method following the methods of stimulation by hydraulic fracturing. The lack of mobile water in the collector was analyzed. An analysis of the effect of the limiting shear gradient on the pressure build-up curve and interpretation results was conducted. According to the results of interpretation by the pressure recovery curve method in the presence of a limiting shear gradient in oil, curves were constructed for various dependences and deviations were revealed.

The geothermal production well often experiences a decline in performance during the production period. The decline of performance can be caused by several factors such as wellbore problem or scale deposition inside the wellbore. Well intervention is a method used to restore the performance of production well. One production well in the Patuha Geothermal Field experienced a decline in performance due to calcite scaling. This well had an initial production of 12.7 MW (based on well testing) but experienced a decline in performance to 2 MW. Several well intervention works have been carried out in this well, such as work over mechanical reaming and acidizing, bullhead acidizing, and well washing. In 2024, the rigless well intervention method was carried out in this well using coil tubing unit for mechanical reaming, hydraulic jetting, and feed zone acidizing. Mechanical reaming was carried out with several bit size to clean out the well. Hydraulic jetting was carried out to optimize the well clean out, but the hydraulic jetting bottom hole assembly (BHA) could not pass to the liner section. Acidizing using a new approach that combined the milling BHA was carried out to dissolve the scale in the feed zone. The results show that the well performance increased by 133% from its previous condition after the rigless well intervention.

Accurate characterization of shaley-sand reservoirs remains a significant challenge in petroleum geophysics, where complex clay mineralogy often renders traditional evaluation methods unreliable. This study introduces an integrated, data-driven framework that synergizes numerical optimization and machine learning ( ML ) to accurately estimate formation water resistivity ( R w ) and predict water saturation ( S w ), overcoming the limitations of data scarcity. The workflow begins with rigorous preprocessing of well log data from 11 wells across the Norwegian North Sea and Egyptian Western Desert. First, we establish a robust, physically-constrained R w by evaluating four optimization algorithms. The Powell and Nelder-Mead algorithms emerged as superior, demonstrating the ability to recover the true Rw from log data with low error (1×10 -4 RMSE) against measured samples rapidly. This optimized R w then serves as a high-quality \"pseudo-core\" label to generate a continuous S w log for training a comprehensive suite of ML models, including ensemble methods (Random Forest, CatBoost, XGBoost) and neural networks (ANNs, LSTM), to predict S w . The models demonstrated predictive accuracy, validated by a robust 5-fold cross-validation protocol. On the blind test wells, the top-performing models (LSTM, CatBoost , and XGBoost) achieved a coefficient of determination (R 2 ) up to 0.944 with Mean Absolute Error ( MAE ) and Root Mean Squared Error ( RMSE ) as low as 0.03 and 0.050 respectively. The automated fusion of optimization-derived physics with ML-driven prediction marks a transformative step toward more reliable, data-centric petrophysical workflows. This integrated framework offers a significant enhancement in reservoir characterization, providing a cost-effective and scalable methodology that reduces reliance on expensive core analyses and improves the accuracy of hydrocarbon-in-place estimations.

Directional drilling often encounters challenges such as eccentric annulus conditions caused by the weight of the drill string and oscillations, compounded by gravity-induced cuttings accumulation that obstructs flow and impedes drilling processes due to inefficient hole cleaning. This study focuses on addressing these issues by developing machine learning (ML) models to predict cuttings concentration (CA) in eccentric deviated wells, aiming to enhance predictive accuracy and optimize hole-cleaning operations. The research employs multiple ML algorithms including back propagation neural network (BPNN), radial basis function network (RBFN), and support vector machine (SVM). Models are trained using comprehensive field data from six deviated wells in the Gulf of Suez, Egypt, with inputs comprising rheological properties, drilling operation parameters, cutting transport velocity ratio (V TR ), and carrying capacity index (CCI). The models undergo rigorous validation to ensure robustness and accuracy, employing both internal validation techniques to avoid overfitting and extensive testing across varying degrees of eccentricity. The developed RBFN model demonstrated superior performance compared to existing empirical and fuzzy logic models, achieving a relation coefficient (R) of 0.993 and an average absolute error (AAE) of 1.18 at an eccentricity degree (ε) of 0.5. In further validation within neighboring test wells, the RBFN model accurately predicted CA across different eccentricities, showing high reliability with R-values of 0.984, 0.978 and 0.971 and AAE-values of 1.1, 1.4 and 1.7 for = 0, 0.4 and 0.8, respectively. Sensitivity analyses confirmed the critical influence of V TR and CCI, with their impact most pronounced at the highest eccentricity tested. This study presents a significant advancement in drilling technology by integrating advanced ML methodologies to improve the monitoring and optimization of hole-cleaning efficiency in deviated wells. The novel application of these sophisticated models offers a promising solution to real-time challenges in drilling operations, enhancing efficiency and reducing operational risks associated with eccentric deviated wells. Incorporating ML models into routine drilling operations can potentially transform standard practices, making this approach a valuable asset in the field of petroleum engineering.

In hydrocarbon reserves, porosity is an important parameter that defines the volume and mobility of the porous fluid. Reservoir and management operations are greatly influenced by porosity. Usually, the standard methods for determining porosity are core analysis and well testing. These methods are very expensive, and generally wells in a field do not have a core. As a result, the methods that can determine the petrophysical properties of the reservoir, including porosity and well logging charts, are very important because well logs are usually available for all wells of a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate reservoir porosity in the shortest possible time using well-logging data. In this study, a new intelligent method of support vector regression with sparrow search algorithm (SVR-SSA) was used to indirectly estimate the porosity of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field). Then, the performance of the hybrid model was compared to that of support vector regression (SVR). A total of 2506 well logging data were included in the database and were divided into two categories of training data (1754 data points) and test data (752 data points) for evaluating models. For the training data set of the SVR-SSA model, R2, mean squared error (MSE), and root mean squared error (RMSE) values were 0.98, 0.000933, and 0.030555, and those for the SVR model were 0.9072, 0.001096 and 0.033108, respectively. Also, for the SVR-SSA model test data set, R2, MSE, and RMSE values were 0.9726, 0.001032, and 0.032128 and those for the SVR model were 0.8931, 0.001660 and 0.040750, respectively. Comparing SVR-SSA and SVR based on R2, MSE and RMSE performance indicators revealed that SVR-SSA outperformed other models in predicting porosity. SVR-SSA is, therefore, a powerful, fast and accurate method of indirectly estimating porosity in reservoirs where porosity is not measured directly in the core.

This study investigates the hydrogeological parameters through detailed pumping test analysis in the Qushtapa and Shamamik areas within the Erbil Basin. This study aims to assess key aquifer parameters, including hydraulic conductivity, transmissivity, and storage coefficient, which are substantial for sustainable groundwater management. Data from 10 groundwater pumping tests were analyzed using established analytical approaches, such as the Cooper-Jacob straight-line method, and the Theis recovery method using AQTESOLV and the Microsoft Excel program to derive hydrogeological parameters of the aquifer. The results show significant spatial variability in aquifer properties impacted by lithological variability and the rate of groundwater recharge. The average hydraulic conductivity ranges between 0.0245 mday-1 and 0.191 mday-1. The average Transmissivity value ranges from 8.62 m2day-1 to 47.725 m2day-1. The Storage Coefficient ranges between 0.071 and 0.323. Based on the Krasny (1993) classification for Transmissivity, the studied area is classified as an area with a Low-Intermediate class of Transmissivity. Pumping tests play a crucial role in ensuring long-term water security and informing sustainable development policies in the study area. تبحث هذه الدراسة في المعايير الهيدروجيولوجية من خلال تحليل مفصل لاختبار ضخ المياه من ابار منطقتي قوشتبة وشمامك ضمن حوض أربيل. تهدف هذه الدراسة إلى تقييم معايير الرئيسية لخزان المياه الجوفية، بما في ذلك الأيصالية المائية، والنفاذية، ومعامل التخزين، والتي تعد مهمة لإدارة المياه الجوفية المستدامة. تم تحليل ضخ المياه الجوفية لـ 10 ابار باستخدام مناهج تحليلية، مثل طريقة الخط المستقيم (Cooper-Jacob) و طريقة استرداد ((Theis باستخدام برنامج AQTESOLV وMicrosoft Excel لاشتقاق المعايير الهيدروجيولوجية للخزان الجوفي. تظهر النتائج تباينًا مكانيًا كبيرًا في خصائص الخزان الجوفي المتأثرة بالتباين الصخري ومعدل تغذية المياه الجوفية. يتراوح متوسط الأيصالية المائية بين 0.0245 م يوم-1 و 0.191 م يوم-1. يتراوح متوسط قيمة النفاذية بين 8.62 م٢يوم-1 47.725 م٢يوم-1. يتراوح معامل التخزين بين 0.071 و 0.323. بناءً على تصنيف كريسني (1993) للنفاذية، تم تصنيف المنطقة المدروسة كمنطقة ذات فئة نفاذية منخفضة إلى متوسطة. تلعب اختبارات الضخ دورًا حاسمًا في ضمان الأمن المائي على المدى الطويل وتوجيه سياسات التنمية المستدامة في منطقة الدراسة.