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This book presents the computational methods for solving the solid mechanic problems in the oil well perforator design. Both Lagrangian and Eulerian methods are used to solve the pertinent stress-strain equations and the shock wave running through the materials. Seven good performance oil well perforators and two conical shaped charges for defeating the reactive armor are included in this book as references. The computer programs written in Fortran for the calculation of high explosive burn time and burn distance, shear modulus and yield strength for many materials, as well as MATLAB plotting programs for many perforators are available online as supplementary materials for the book-- Provided by publisher.

Satellite gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) provide quantitative measurement of terrestrial water storage (TWS) changes with unprecedented accuracy. Combining GRACE-observed TWS changes and independent estimates of water change in soil and snow and surface reservoirs offers a means for estimating groundwater storage change. Since its launch in March 2002, GRACE time-variable gravity data have been successfully used to quantify long-term groundwater storage changes in different regions over the world, including northwest India, the High Plains Aquifer and the Central Valley in the USA, the North China Plain, Middle East, and southern Murray-Darling Basin in Australia, where groundwater storage has been significantly depleted in recent years (or decades). It is difficult to rely on in situ groundwater measurements for accurate quantification of large, regional-scale groundwater storage changes, especially at long timescales due to inadequate spatial and temporal coverage of in situ data and uncertainties in storage coefficients. The now nearly 13 years of GRACE gravity data provide a successful and unique complementary tool for monitoring and measuring groundwater changes on a global and regional basis. Despite the successful applications of GRACE in studying global groundwater storage change, there are still some major challenges limiting the application and interpretation of GRACE data. In this paper, we present an overview of GRACE applications in groundwater studies and discuss if and how the main challenges to using GRACE data can be addressed.

\"Subjective Well-Being and Social Media shows how, by exploiting the unprecedented amount of information provided by the social networking sites, it is possible to build new composite indicators of subjective well-being. These new social media indicators are complementary to official statistics and surveys, whose data are collected at very low temporary and geographical resolution. The book also explains in full details how to solve the problem of selection bias coming from social media data. Mixing textual analysis, machine learning and time series analysis, the book also shows how to extract both the structural and the temporary components of subjective well-being. Cross-country analysis confirms that well-being is a complex phenomenon that is governed by macroeconomic and health factors, ageing, temporary shocks and cultural and psychological aspects. As an example, the last part of the book focuses on the impact of the prolonged stress due to the COVID-19 pandemic on subjective well-being in both Japan and Italy. Through a data science approach, the results show that a consistent and persistent drop occurred throughout 2020 in the overall level of well-being in both countries. The methodology presented in this book: enables social scientists and policy makers to know what people think about the quality of their own life, minimizing the bias induced by the interaction between the researcher and the observed individuals; being language-free, it allows for comparing the well-being perceived in different linguistic and socio-cultural contexts, disentangling differences due to objective events and life conditions from dissimilarities related to social norms or language specificities; provides a solution to the problem of selection bias in social media data through a systematic approach based on time-space small area estimation models. The book comes also with replication R scripts and data. Stefano M. Iacus is full professor of Statistics at the University of Milan, on leave at the Joint Research Centre of the European Commission. Former R-core member (1999-2017) and R Foundation Member. Giuseppe Porro is full professor of Economic Policy at the University of Insubria. An earlier version of this project was awarded the Italian Institute of Statistics-Google prize for \"official statistics and big data\"\"-- Provided by publisher.

Detecting water head is crucial in groundwater utilization, and requires quick and accurate solutions. This study employs a method combining the collocation Trefftz method (CTM) and the fictitious time integration method (FTIM) for groundwater head detection and restoration. The Laplace equation is solved using CTM, and the Trefftz basis function is linearly combined to fit the exact solution while adding characteristics length for numerical stability. The FTIM solves the nonlinear algebraic equations system for water head detection. Numerical examples quantify the method's accuracy, and boundary restoration results are compared with the Picard successive approximation method, showcasing FTIM's advantages in convergence steps and precision. The solution comparison under irregular boundary conditions further verifies the proposed method's efficacy (MAE ≤ 10 –15 ). The CTM–FTIM calculated water level boundary aligns with the actual boundary, and its noise immunity is verified using real observation well data (MAE ≤ 10 –2 , Itertimes ≤ 5000). The CTM–FTIM method eliminates meshing needs for quick solutions in irregular regions, accurately determining water levels in the study domain using few known boundary points, solving infinite domain groundwater head detection.

Well log analysis is significant for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir evaluation. Addressing these challenges requires effective methods for both synthetic data generation and precise imputation of missing data, ensuring data completeness and reliability. This study introduces a novel framework utilizing sequence-based generative adversarial networks (GANs) specifically designed for well log data generation and imputation. The framework integrates two distinct sequence-based GAN models: time series GAN (TSGAN) for generating synthetic well log data and sequence GAN (SeqGAN) for imputing missing data. Both models were tested on a dataset from the North Sea, Netherlands region. For the imputation task, the input comprises logs with missing values and the output is the corresponding imputed logs; for the synthetic data generation task, the input is complete real logs and the output is synthetic logs that mimic the statistical properties of the original data. All log measurements are normalized to a 0-1 range using min-max scaling, and error metrics are reported in these normalized units. Different sections of 5, 10, and 50 data points were used. Experimental results demonstrate that this approach achieves superior accuracy in filling data gaps compared to other deep learning models for spatial series analysis. The imputation method yielded values of 0.92, 0.86, and 0.57, with corresponding mean absolute percentage error (MAPE) values of 8.320, 0.005, and 166.6, and mean absolute error (MAE) values of 0.012, 0.002, and 0.03, respectively. The synthetic generation yielded of 0.92, MAE, of 0.35, and MRLE of 0.01. These results set a new benchmark for data integrity and utility in geosciences, particularly in well log data analysis.

In clay-sand reservoirs, shale volume affects porosity and permeability, with porosity governing storage capacity; these properties influence reserve and productivity predictions, which directly affect reservoir and economic assessments. Estimates of porosity and shale volume from independent log-based methods may introduce coupled biases, whereas those from joint inversion better honor their interdependence. Joint inversion has traditionally relied on simplified assumptions or extra data; in contrast, recent data-driven approaches capture complex log patterns. However, purely data-driven methods suffer from feature-target shifts and cannot enforce inter-target dependencies. To address these limitations, a two-stage deep learning framework combining self-supervised log modeling with core-calibrated low-rank adaptation (CCLoRA) is proposed for joint porosity and shale volume prediction. First, a Conditional Score-based Diffusion Imputation (CSDI) model is self-supervised on synthetic logs generated from empirical formulas. This enables learning of plausible log sequence structures and confers partial robustness to feature-target shifts without extensive labeled data. Second, core-scale petrophysical relationships are transferred to the log scale through well-specific feature replacement using CCLoRA. This corrects residual feature-target shifts and enforces inter-target dependencies between the two parameters with minimal fine-tuning cost. Experiments on well-consolidated sandstones show the full pipeline outperforms multiple deep learning baselines, delivering accurate and physically consistent estimates.

At the Department of Earth and Environmental Sciences of the University of Milano-Bicocca (DISAT-UNIMIB), a hydrogeological well database, called TANGRAM©, has been developed and published on line at www.TANGRAM.samit.unimib.it, developing an earlier 1989 DOS version. This package can be used to store, display, and process all data related to water wells, including administrative information, well characteristics, stratigraphic logs, water levels, pumping rates, and other hydrogeological information. Currently, the database contains more than 39.200 wells located in the Italian region of Lombardy (90%), Piedmont (9%) and Valle d’Aosta (1%). TANGRAM© has been created both as a tool for researches and for public administration’s administrators who have projects in common with DISAT-UNIMIB. Indeed, transferring wells data from paper into TANGRAM© offers both an easier and more robust way to correlate hydrogeological data and a more organized management of the administrative information. Some Administrations use TANGRAM© regularly as a tool for wells data management (Brescia Province, ARPA Valle Aosta). An innovative aspect of the database is the quantitative extraction of stratigraphic data. In the part of the software intended for research purposes, all well logs are translated into 8-digit alphanumeric codes and the user composes the code interpreting the description at each stratigraphic level. So the stratigraphic well data can be coded, then quantified and processed. This is made possible by attributing a weight to the digits of the code for textures. The program calculates the weighted percentage of the chosen lithology, as related to each individual layer. These extractions are the starting point for subsequent hydrogeological studies: well head protection area, reconstruction of the dynamics of flow, realization of the quarry plans and flux and transport hydrogeological models. The results of a two-dimensional distribution of coarse, medium and fine sized material in the first 80 meters of depth are presented here for a study area located within the Province of Brescia (Italy).

This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model’s performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study’s findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels.

This study investigates natural hydrogen (H2) occurrences in the Paris Basin, using Optical Character Recognition (OCR) technology to analyze an extensive, yet underexploited, database that contains historic drilling records. The potential of natural hydrogen has been largely unexplored in conventional oil and gas wells. Utilizing the in‐house CVAGeoDB database based on public well data, which includes well logs, mudlogs, and End Drilling Reports (EDRs) in PDF image format, we applied the Tesseract‐OCR Engine to convert these documents into searchable formats for efficient data analysis. Our analysis revealed several H2‐bearing wells across French sedimentary basins. The hydrogen occurrences in the Aquitaine Basin may be explained by the geological context and in particular the presence of a mantle body at shallow depth. On the contrary, the detection of H2 in the Paris Basin cannot be explained in a straightforward manner as the presence of ultramafic or U‐rich rocks is poorly documented so far. In the Paris Basin, H2 has been detected in four main formations: the Lusitanian, the Dogger, and Triassic aquifers as well as in the basement. The highest hydrogen concentration (52 vol%) was measured in the Dogger aquifer. These wells are primarily located along the Bray Fault, indicating at least a structural influence on H2 distribution. Finaly, the presence of serpentinzed dunite from the Lizard complex associated with the bedrock may have played the role as a source for H2. This research demonstrates the effectiveness of OCR in reassessing historical drilling data for natural hydrogen exploration, highlighting the need for comprehensive exploration methodologies in this emerging field. Plain Language Summary This study explores the presence of natural hydrogen (H2) in the Paris Basin, employing Optical Character Recognition (OCR) technology to sift through an extensive database of old drilling records that have not been fully utilized in the past. As the world increasingly seeks carbon‐neutral energy sources, natural hydrogen produced through interactions between water and rocks emerges as a promising alternative to fossil fuels. Our research focuses on the CVAGeoDB database, which contains detailed information on drilling activities but in a non‐searchable PDF image format. OCR is a tool that turns images containing text, such as scanned documents, into text files that one can easily search and analyze. Our findings indicate the presence of H2 in several wells across French sedimentary basins. The Paris Basin, exhibits unexpected H2 occurrences not directly linked to anticipated geological factors classically used in H2 exploration. In the Paris Basin, the highest hydrogen concentration (52 vol%) was discovered in the Dogger aquifer. These wells are predominantly situated along the Bray Fault, suggesting a structural control on the distribution of hydrogen. This research underscores the utility of OCR in re‐evaluating historical drilling data for natural hydrogen exploration. Key Points Natural hydrogen exploration in former oil and gas province Use of the Optical Character Recognition algorithm to optimize processing of a large drilling report database Indices for a potential H2 system (source, migration, trap) in the Paris Basin

There is increasing interest in using Gravity Recovery and Climate Experiment (GRACE) satellite data to remotely monitor groundwater storage variations; however, comparisons with ground-based well data are limited but necessary to validate satellite data processing, especially when the study area is close to or below the GRACE footprint. The Central Valley is a heavily irrigated region with large-scale groundwater depletion during droughts. Here we compare updated estimates of groundwater storage changes in the California Central Valley using GRACE satellites with storage changes from groundwater level data. A new processing approach was applied that optimally uses available GRACE and water balance component data to extract changes in groundwater storage. GRACE satellites show that groundwater depletion totaled ∼31.0 ± 3.0 km3 for Groupe de Recherche de Geodesie Spatiale (GRGS) satellite data during the drought from October 2006 through March 2010. Groundwater storage changes from GRACE agreed with those from well data for the overlap period (April 2006 through September 2009) (27 km3 for both). General correspondence between GRACE and groundwater level data validates the methodology and increases confidence in use of GRACE satellites to monitor groundwater storage changes.