bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graph-Level Feature Embedding with Spatial– Temporal GCN Method for Interconnected Well Production Forecasting
Authors: Xu Ziming , Leung Juliana Y. 
Published: September 2024

Abstract
Deep-learning models have been employed for production forecasting in oil and gas reservoirs, but they often assume that each well operates independently, neglecting the connectivity and dynamic interactions between wells. This simplification can significantly compromise prediction accuracy. Therefore, graph convolutional networks (GCNs) have been applied to incorporate data from neighbouring wells. However, existing spatial-temporal GCN (ST-GCN) methods are mainly used for autoregressive tasks and face limitations in predicting newly developed wells and fully utilizing temporal neighbour interactions. This study introduces an ST-graph- level feature embedding (ST-GFE) method that provides accurate production forecasting for newly developed wells. It enhances forecasting by aggregating the historical data from neighbouring wells into a single feature vector. This aggregated vector, merging local and contextual information, contains richer information about the studied region.
We evaluate ST-GFE using a dataset of 6,605 Montney shale gas wells, incorporating formation properties, fracture parameters, and production history. The ST-GFE is integrated with a non-autoregressive encoder-decoder structure to do production forecasting. The findings demonstrate that ST-GFE significantly improves prediction accuracy for newly developed wells compared to the purely temporal models, such as recurrent neural network (RNN)-based and Transformer models. ST-GFE adapts to production changes in adjacent wells, providing accurate predictions across various application scenarios, including shut-in and in-fill drilling activities. Additionally, while traditional GCNs require a full-batch training approach that leads to scalability issues, the ST-GFE model treats each well and its surrounding wells as a graph, enabling batch training and significantly reducing memory usage. Furthermore, the model dynamically updates its forecasts with real-time production data, enhancing precision and relevance. Experimental results confirm that ST-GFE effectively leverages spatio-temporal dynamics and interactions between adjacent wells, further improving production forecasting accuracy. This method enhances predictions and generalization capabilities for new developing locations, broadening its applicability to various drilling and production scenarios.
Quantitative Laboratory Assessment of Caprock Integrity for Geological Carbon and Hydrogen Storage
Authors: Nath F. , Cabezudo E. , Romero N. G. 
Published: September 2024

Abstract
Caprock integrity is a critical factor in geological carbon storage (GCS) and hydrogen storage (GHS), as it ensures containment assurance, environmental and safety considerations, storage efficiency, risk mitigation, long-term viability, public and regulatory confidence, economic considerations, and informed decision-making. Caprock can break mechanically by radial cracking, plastic deformation, inner and outrebounding, and channeling. Understanding these failure mechanisms is crucial, but traditional deformation tracking methods have limitations. To address this gap, this study utilizes three-dimensional digital image correlation (3D-DIC) to investigate the deformation and failure of caprock specimens from GCS and GHS, thereby ensuring their integrity.
Caprock samples were compressed diametrically using a precision 100 kN electromechanical load frame moving at 0.05 mm/min. There were a variety of minerals in the samples that were examined. During the diametrical compression testing, a 3D-DIC system was set up to monitor the samples without touching them at a rate of 10 frames per second. A black-and-white speckle pattern is applied to the specimen to monitor its deformation under stress. The 3D-DIC system is used to process, visualize, and analyze caprock sample damage under varied load conditions.
A load-strain relationship was established by integrating several factors: 1) DIC-generated horizontal, vertical, and shear strain maps, providing insight into the sequence of strain development and fracture occurrences attributed to pre-existing or natural fractures, anisotropy, heterogeneity, laminations, faults, cracks, or channeling. 2) The results demonstrated a 4-stage deformation evolution process: initial, linear elastic, elastic-plastic, and plastic damage. 3) Additionally, the study illustrated the failure process of caprock samples, yielding tension and compression within ±0.05%, shear within ±0.001%, and a tension-compression ratio ranging from 1% to 5%. 4) three failure patterns were observed in the tested caprock specimens, including central, deviated from the center, and mixed modes due to tension, compression, and shear.
Injecting H2 or CO2 into the formation can lead to increased pore pressure and caprock failure in geological carbon and hydrogen storage projects. Geomechanical risks such as caprock instability, fault slip, and formation deformation can occur throughout the project's lifespan. There is no perfect methodology that covers all the problems in caprock integrity research. This study focuses on predicting caprock deformation and failure using non-contact optical methods, with 3D-DIC being more effective in terms of test range, affordability, accuracy, and full-field monitoring. The image-based algorithm is better at understanding anisotropic and heterogeneous conditions, enhancing the inspection of caprock integrity in underground carbon and hydrogen storage projects.
Human Factors Ecosystem – How Human Performance and Process Safety Builds the Energy Transition and a Low-Carbon Future for O&G Industry
Authors: França J. E. M. 
Published: September 2024

AbstractThe transition to a low-carbon future in the O&G industry seeks a comprehensive understanding of the Human Factors Ecosystem, particularly in relation to human performance and process safety. This paper explores the critical role that human factors play in fostering an energy transition, highlighting how optimized human performance and enhanced process safety can significantly contribute to the industry's sustainability goals. The study delves into the interplay between human behavior, organizational culture, and technological advancements, emphasizing the need for a systemic approach to manage the inherent complexities and risks of high-stakes environments. By examining case studies and applying methodologies such as the Functional Resonance Analysis Method (FRAM), the paper illustrates how a systemic and integrative Human Factors approach can lead to more resilient and adaptable operations. The findings underscore the importance of integrating different disciplines into the strategic planning and operational frameworks of O&G companies to drive efficiency, safety, and environmental stewardship. This research provides valuable insights for industry leaders, policymakers, and safety professionals aiming to align human performance with the overarching goals of the energy transition, adopting practical cases studies of accident analysis, human reliability and risk analysis with FRAM.
Rethinking Surfactant Huff-n-Puff for Enhanced Oil Recovery in the Bakken: Recent Pilot Field Study Utilizing Multifunctional Biosurfactants
Authors: Pearl M. , Nelson E. , Rothe J. , Black K. , Muus P. , Dittus A. , Shumway M. , Kakadjian E. , Au Yong H. 
Published: September 2024

Abstract
Since November 2019, the Bakken shale play has experienced a decline of 6% annually to the current production of 1,100 Mbbl/d. With decline rates as high as 85% over the first three years of production and a primary recovery rate of 3-10% of original oil in place, finding ways to recover more oil using EOR techniques in this maturing unconventional play is key. In recent years, huff-n-puff EOR techniques were typically regarded as the most cost- effective methods for unconventional plays. However, novel biotechnology, proven in multiple US Basins and now expanded to Bakken for the first time in 2021, has shown very promising results. This paper will provide details of a pilot study conducted on multiple wells, showcasing the potential of this innovative approach.
The treatment fluid, a biosurfactant-based solution, was successfully applied in the field by pumping an aqueous solution through the tubing-casing annulus of each rod-pumped well. Treatment volumes were pumped between 13- 15 bbl/minute with a top pressure of 1,400 psi observed. Post-application produced fluid samples were regularly collected and analyzed to monitor the residual impact of biosurfactants, reassuring us of the effectiveness of this method.
The baseline production for each initial test well was approximately 20 bbl of oil, 20 Mcf of gas, and less than 10 bbl of water per day. Following the treatments, the wells were returned to production after a shut-in period of less than 8 days. Upon return to production, each test well achieved peak oil production of more than 70 barrels per day after just 5 days of run-time, with peak water production of 110 barrels per day achieved after 2 days of run-time. After 125 days of run-time, the average oil production per well was maintained at greater than 32 barrels per day, a 70%+ increase over forecast oil volumes. To date, the total oil recovered above pre-treatment forecasts has been greater than 1,700 bbl for each well, and the total water recovered above forecast was less than 20% of the treatment volume for each well. The pilot study has demonstrated that this new, sustainable technology based on multifunctional biosurfactants provided a significant and impressive production boost, instilling optimism for the future of enhanced oil recovery in the Bakken shale play.
Nonlinearly Constrained Life-Cycle Production Optimization Under Geological Uncertainty: A Realistic Application
Authors: Toktas Omer Lutfu , Nguyen Quang Minh , Onur Mustafa , Alpak Faruk Omer 
Published: September 2024

Abstract
The objective of this study is to present a realistic application of our previously developed nonlinearly constrained stochastic gradient-based optimization framework that can efficiently solve nonlinearly constrained robust production optimization problems. We apply the optimization framework to a synthetic, yet realistic, field optimization problem using multiple history-matched realizations of the geologic model. For the synthetic realistic field example, the reservoir is assumed to undergo production for several years. Well controls (BHPs for producers and water injection rates for injectors) on a sequence of control steps (time intervals) are optimized during the forecast period by maximizing the expected value of the net present value (or ensemble average NPV) over multiple realizations of subsurface description for the subsequent 10 years of production under a waterflooding scenario subject to nonlinear constraints. The optimization framework is based on our in-house nonlinear optimizer employing sequential quadratic programming (SQP) coupled with stochastic simplex approximated gradients (StoSAG). It incorporates the line-search procedure within SQP and is referred to as line-search sequential quadratic programming (LS-SQP). We couple LS-SQP with two different constraint handling schemes; the expected value constraint scheme and minimum-maximum (min-max) constraint scheme, to avoid the explicit application of nonlinear constraints for each reservoir model. Both bound constraints on well controls and nonlinear constraints on field liquid production rate, field water injection rate, and/or individual producer water production rates are considered. We consider several different history-matched geological realizations of the realistic field example with several injectors and producers. A black-oil commercial reservoir simulator is utilized in the optimization workflow to assess the objective function with imposed constraints. Results show that the framework LS-SQP algorithm with StoSAG can effectively handle the nonlinear constraints in a robust life-cycle production optimization problem. Moreover, our results show that the LS-SQP framework with any of the two different constraint handling schemes considered effectively handles the nonlinear constraints in a life-cycle robust production optimization problem. However, the expected value constraint scheme results in higher optimal NPV than the min-max constraint scheme, but at the cost of possible constraint violation for some individual geological realizations. Application results demonstrate that our nonlinearly constrained stochastic gradient-based robust optimization framework has great potential to be a rapid approximate capability for performing life cycle production optimization under subsurface uncertainty with bound and nonlinear constraints.
Realtime Caving Prediction for Drillstring Packoff Simulation Using Data-Driven Model Coupled with Geomechanics
Authors: Liu Zhengchun , Zhang Yuan , Samuel Robello 
Published: September 2024

Abstract
During well construction operations, when the drill cuttings are not transported efficiently out of the well, or if parts of the wellbore collapse on the drillstring because of an unstable formation, the well can begin to pack off. There are a lot of models to estimate the volume of drilling cuttings. But few literatures can be found for volume estimation of cavings from stress-induced wellbore damage.
Caving volume is predicted using a geomechanical caving model. Assuming the caving debris has the shape of a prism, caving volume can be estimated as a function of wellbore radius, caving depth, caving length, and the breakout angle. Breakout angle is estimated as a function of rock strength, geostress and wellbore pressure. The effects of wellbore inclination and azimuth are also taken into account for deviated wells. Caving depth is typically obtained from the caliper log, which is usually not available during drilling operations. Therefore, hybrid engineering and data-driven approach are employed to predict the caving depth.
Different data-driven models (e.g., Random Forest) were developed and tested for predicting caving depth h, assuming it is a function of rock geomechanical properties, geo-stresses, pore pressure, wellbore pressure, and gamma ray log. Caliper data log data of multiple wells are used to train and cross-validate those data-driven models. Cross-validation results indicate that three data-driven models including random forest, neural network, and Adaboost are performing very well.
The simulation method can be applied for real-time monitoring of pack-off risk during drilling operations. In the workflow, real-time survey data (measured depth, inclination, azimuth) and rig-state data, including measured depth (MD), gamma ray (GR), flow rate, rotational speed (RPM), rate of penetration (ROP), weight on bit (WOB), etc., are continuously received by the computer and buffered in caches. These buffered data are then consumed by the geomechanical caving width model and the data-driven caving depth model. As a result, real-time caving volume is calculated at every depth of interest.
Combing both real-time cuttings data and real-time caving data, a real-time packoff volume percent can be obtained and displayed in a continuously updating plot. The approach is based on synthesis of an engineering model and a data-driven model.
2023 SPE Diversity and Inclusion Survey: Driving Equality in the Energy Industry
Authors: Carreras P. E. , Guiñazu M. L. 
Published: September 2024

Abstract
An SPEi survey was launched in May 2023 to determine the status of Diversity and Inclusion (D&I) in the energy industry from the workforce perspective. Besides, survey results were compared with the results of a similar survey launched in 2020. The survey consisted of seven demographics, fifteen multiple-choice and five open-ended questions overall similar to the questions from the 2020 survey. A few additions aimed to capture changes in the workplace due to the pandemic and the ongoing energy transition. The proposal was reviewed and approved by the SPE D&I committee. SPE Marketing Research Group submitted the survey to SPE members and non-members located worldwide. The survey was also promoted on social media to increase its reach. The responses were analyzed with the help of charts and tables that summarized key findings. More than 900 responses were received from all over the world. 74% were men, 21% women, 1% non-binary and 4% preferred not to answer the gender question. Most of the participants were from North America (58%) and Western Europe (12%), followed by Asia (8%), South America (8%) and Africa (7%). 96% of the respondents held college degrees. Their ages were 26-35 years old (12%), 36-45 years old (20%), 46-55 years old (17%), 56-65 years old (21%) and older than 66 years old (23%). 58% worked for upstream, 14% for oil field services and equipment and 13% were consultants. Most professionals have positive perceptions about the industry's benefits, work environment, and contributions to the energy transition, top conditions to recruit and retain younger generations. Most respondents prefer to work virtually or part time from home. Women consider that they face gender biases at work and that their careers are limited by negativity or discrimination by management. While some participants express skepticism or opposition to D&I initiatives, the majority emphasize the importance of creating an inclusive work environment. Hiring, assignments and promotions were supported based on merit regardless of differences or merit without using quotas to benefit minorities. The understanding of women and men about important D&I topics differs. For example, majority of women perceive that, even today, they are not treated equally at work, while majority of men perceive that everyone is treated equally. D&I has matured to include not only aspects of identity (gender, race) but also aspects of the value and benefits obtained by promoting diversity of thoughts, making merit-based decisions, challenging biases, and fostering equal opportunities for all employees. The different opinions that women and men have expressed in the survey illustrate how organizations can be nurtured by the presence of diverse individuals that bring their unique perspectives and contributions for long-term success.
Gas Lift Anomaly Detection in Unconventional Fields Using Expert System Techniques
Authors: Zejli A. , Shrestha A. , Cormier B. 
Published: September 2024

Abstract
Production engineers managing unconventional gas lifted wells face ever-changing operational conditions with newly drilled wells coming online, on-going well workover, facility upsets and artificial lift configuration changes. Optimizing gaslift operations involves delivering optimal gaslift injection to wells while respecting operational constraints but also detecting misbehaving wells early to mitigate underperformance. Detecting anomalous behavior in gaslift wells is onerous due to the dynamic nature of the operations and the deluge of field measurements that need to be analyzed.
This paper will cover a novel approach to detecting anomalous behavior, suboptimal production, and integrity issues in gaslift wells in unconventional fields. The approach relies on using expert system techniques in conjunction with physics-based models to detect these conditions and notify engineers to take remedial action. The paper will also cover how this novel approach was built into a scalable and extensible solution called the Gas Lift Anomaly Detection (GLAD) tool.
Preliminary Research on Applications of Large Language Models in E&P Industry
Authors: Zhou X. G. , Jing X. W. , Wu W. K. , Gao G. Z. 
Published: September 2024

Abstract
The objective of this paper is to develop EPChat, an industry-specific large language model tailored for the exploration and production (E&P) field through retrieval-augmented generation (RAG) and fine-tuning techniques. This novel EPChat model effectively mitigates the common issue of hallucinations inherent in generic LLMs, thereby enabling it to function as a reliable AI assistant in the oil and gas industry, providing accurate and contextually relevant responses.
Real Time Application of Deep Learning Based Flare Smoke Detection
Authors: Gupta Kratika , Chatterjee Purba , Chen Xianzhong John , Kadam Jitendra 
Published: September 2024

Abstract
This work focuses on developing a smoke detection model that can be employed in an automated, end-to-end flare smoke detection, alerting and distributed control solution that leverages existing flare Closed Circuit Television (CCTV) cameras used at manufacturing facilities. At the core of this solution is a deep-learning computer vision model that is developed leveraging an extensive and diverse data set and the model has demonstrated fit-for-use performance in wide-ranging conditions. The smoke detection model uses a novel approach for real time detection of hydrocarbon emission leveraging semantic segmentation, through compact vision transformers. This innovative approach enables real-time identification of smoke in flare images, which is a key control signal for an efficient closed-loop system.
The training dataset includes a rich diversity of flare images (collected from global locations and environment conditions), including steam images with blank smoke masks to mitigate false positives. Additionally, images with minimal smoke were included to enhance the model's sensitivity to light smoke detection. External testing was conducted on previously unseen datasets, ensuring the model's robustness and generalizability. The developed model performs robust smoke detection with high precision alerting and minimizing false positives from steam images. This application has been deployed in the plant within an end-to-end solution, creating a positive impact on operations.