bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automating Industrial Training Development Using Text to Speech
Authors: Tharayil Sarafudheen , Abo Shal Mazen 
Published: March 2023

Abstract
Developing industrial training material for an oil and gas company is challenging, as many of the specialized skills will be confidential and training needs to be tailored based on the characteristics of the operations. So, oil and gas companies spend huge amounts on developing customized training materials. During the training development, recording training material is one of the most challenging tasks. This solution will provide a platform, which will automate the conversion of texts to audio and integrate with industrial online courses to generate natural soundtracks utilizing artificial intelligence and machine learning technology, especially with the advancement of Natural Language Processing (NLP). The automated mechanism of speech synthesis helped the training developers to speed up preparing training materials, reduce the human mistakes, and standardize the training modules. Results showed no major differences in learning outcomes and naturality between the actual recording and machine recordings – allowing automation and simulating a realistic user experience.
Enabling Reliable Stimulation of Multiple Perforated Intervals in a Thick Laminated Reservoir: Lessons Learnt in Successful Sequenced Fracturing Evaluation
Authors: Asif Adnan , Khan Abdul Muqtadir , Alaqeel Abdulaziz , Kayumov Rifat 
Published: March 2023

Abstract
Near-wellbore fracture diversion is a common means of achieving uniform stimulation in long wellbores through heterogeneous reservoirs. A novel sequenced fracturing technique using self-degradable, nondamaging, multimodal particulate pills was deployed for the first time in a clastic rock formation in the Middle East. Application of an advanced design and evaluation workflow to stimulate two perforated intervals in a single continuous pumping operation is presented.
Sequenced fracturing with two-step diversion was combined with channel fracturing to increase the chance of successful placement and reduce the inherent risk of screenout. Two proppant ramps were pumped, separated by a diverter pill composed of a multimodal particulate blend and fibers. The size distribution and volume of particles were optimized to create a low-permeability plug in the entry of the primary fracture. The pills were designed so that large particles would intercept the fracture entrance and smaller ones would reduce the permeability of the formed plug, while the fibers would help to maintain the plug integrity both during pumping and once it formed in the near wellbore region.
Diversion was confirmed by a pressure increase of 800 psi during the placement of the pill, followed by a new breakdown signature and a treating pressure increase of 500 psi at the same pumping rate. The diversion design was optimized based on the temperature log before fracturing to evaluate the preferential fracture geometry. The evaluation was conducted with a nonradioactive traceable proppant that was pumped in the second proppant ramp to assess the proppant placement using a neutron log. Consequently, a production log was conducted followed by pressure diagnostics. Since the diagnostic results indicated some ambiguity, for a way forward in the field development, the upper interval was refractured and showed high pressures and early treatment termination. The entire flowback timeline was compared, and the productivity index showed no change before and after refracturing. Therefore, multiple pressure diagnostics, post-fracturing flowback enhancement, and the overall intense evaluation verified proppant placement in both perforated intervals using the sequenced fracturing technique. The technique has the potential to save up to 10 days of completion time, which was calculated to yield approximately 40% time and cost savings.
This paper presents a complex stimulation treatment analysis that can be utilized to apply the lessons learnt in design and evaluating the success of diversion treatments to aid future treatments. This proven technology enhances efficiency and economics, especially in multistage fracturing, where the efficiency could be enhanced by three- to fivefold.
Industrial Training Recommendation Systems
Authors: Veettil Tharayil Sarafudheen , Almass Abdulaziz , Dursun Serkan , Al Mudaifer Danah , Al Qahtani Rahaf 
Published: March 2023

Abstract
Talent management in oil and gas is considered as one of the crucial challenges. The solution requires understanding talents based on training and career development. In this paper, Artificial Intelligence (AI) based solution is conceptualized and implemented using an ensemble machine learning algorithm that provides a recommendation system derived from industrial training history. The developed AI solution combines machine learning algorithms, knowledge from subject matter expert and user profile analysis suggestions. The proposed solution utilizes different machine learning mechanisms such as recommender engines, Natural Language Processing (NLP) and distance measures. The mechanism also gives an end-to- end solution including data ingestion, data processing, model building and visualization with a data consumption layer. Compared to the models available in the academia and industry, we combine the machine learning, NLP text analytics and domain expertise to provide accurate recommendation. The recommendation has unique ensembled models for each industrial area within the corporate.
Using Tracer-Based Technologies for Production Profiling in a Hydraulic Fracture to Optimise the Hydraulic Fracturing Design
Authors: Upadhye Vishwajit , Malyavko Evgeny , Kashapov Denis 
Published: March 2023

Abstract
The tracer-based surveillance technology can be used in hydraulic fractures to perform production profiling surveillance continuously for several years without the need for expensive and operationally risky interventions. The paper deals with the case of placing tracer coated marked proppant with different signatures in each hydraulic fracturing stage. In particular, it dwells on the experience of placing different signatures of tracer coated marked proppant within one port to evaluate the production dynamics of the far and near zones of a hydraulic fracture. The paper also offers methods for optimizing the hydraulic fracturing design to increase the hydraulic fracture production period and the cumulative production.
Corporate Sustainability Culture in Oil and Gas Industry – Enablers and Challenges
Authors: Fryc Alicja , Marshall Brown Caroline 
Published: March 2023

Abstract
The paper shares learnings from building a corporate sustainability culture as an infrastructure for environmental and sustainability programs by identifying enablers and challenges common to the oil and gas organisations which are relatively advanced in their sustainability journey. The study presents conclusions from a survey conveyed within oil and gas industry and presents learnings in the context of the theoretical exploration of the subject of organisational sustainability culture in recent literature.
A closer look at the successes and challenges in developing sustainability and environmental culture within the petroleum industry so far, leads to identification of three main success factors: (1) Strong corporate commitment, visible in practice and genuinely committed leadership; (2) Translating corporate sustainability approach into economic value (such as business development advantage, technological innovation agility, energy savings, cost reduction); (3) Communication of targets and progress towards them and cascading sustainability objectives down to organisational units. The study identifies several prevailing challenges, including: (1) Lack of resources; (2) Lack of leadership or visibility of it and (3) scepticism around either the positive role of oil and gas in climate action or the authenticity of corporate sustainability efforts.
When comparing survey respondents’ experience of the organisational sustainability culture to its theoretical models, it can be concluded that (a) sampled organisations’ programs are most developed in the areas suggesting external drivers, such as legislative and customer requirements or public opinion pressure, (b) sustainability vision, corporate values and their communication are seen as the most culture enabling elements of the theoretical model in practice, and (c) challenges in building a sustainability culture are present in all main elements of the theoretical model, likely indicating that the industry is still gaining the experience and developing its approach and solutions for sustainability.
Conclusions and implications can be utilised by organisations in early stages of designing sustainability and environmental stewardship programs to consider maximizing the identified success factors while mitigating the challenges to build a sustainability culture that enables organisations to respond to environmental and social demands and build organisational resilience.
Efficient Implementation of Reverse Time Migration Seismic Imaging on FPGAs
Authors: Ibrahim Alhussain , Alsultan Abdullah , Elrabaa Muhammad , El-Maleh Aiman , Tonellot Thierry 
Published: March 2023

Abstract
This work presents an alternative computing platform, Field Programmable Gate Arrays (FPGAs), for one of the widely used applications in the oil and gas industry; seismic imaging using reverse time migration (RTM). As with many of the oil and gas applications, RTM involves many computations performed repeatedly over extremely large number of data points. Conventionally, such computations were implemented as a sequence of temoral steps and parallelized using multi-core CPUs or GPUs at various levels of abstractions (task, thread, and instruction-levels). With FPGAs, computations can be parallelized and implemented as spatially separated steps (i.e. the computing fabric can be configured to carry out all the computations, side-bu-side rather than as temoral steps). In this work, we illustrate the spatial computing capabilities of FPGAs using RTM. Two different implementations with different types of optimizations were developed. Performance optimizations and measurements based on experimental test bed are reported.
An Algorithm for Consistent Mechanical Earth Model Parameters Determination: Applications in Wellbore Stability and Hydraulic Fracturing Modeling
Authors: AlBahrani Hussain , Alkhaldi Yara 
Published: March 2023

Abstract
A mechanical earth models (MEM) is a crucial component of any well design process. Because it describes the subterranean formations in-situ stresses and rock mechanical properties, it is the main input into any geomechanics model. This means high impact issues like wellbore instability, drilling induced fractures, and hydraulic fracturing success probability are all directly dependent on the reliability of the provided MEM. Considering that MEM have been historically developed based on empirical correlations and an expert manual interpretations of logging data, intuitively, their accuracy has a large margin for improvement. Specifically, because the determination of the in-situ maximum horizontal stress is based on manual interpretations of caliper or image logs, the result of this interpretation can vary greatly from one user to the other. Therefore, this work aims to improve this process by presenting an automated algorithm that interprets image and caliper logs data and produce a consistent determination of different MEM parameters.
The proposed algorithms utilize both resistivity and ultra-sonic based image logs along with wireline caliper measurements to first quantify areas of enlargements or wash-outs within a wellbore section. The algorithms perform this quantification by reading the Red-Green-Blue (RGB) values of each pixel in the image and then assign a radial measurement to that reading. The generated radial measurements are calibrated using the caliper log data. The end result of this process is radial measurements that describe the wellbore dimensions across the 360° range. These radial measurements are then filtered for enlargements by comparing them to the size of the drill bit that was used to drill the hole section being analyzed. Finally, the output from these algorithms is utilized within a stress polygon to determine the in-situ maximum horizontal stress.
To showcase the value of consistent estimations, MEM produced through the proposed algorithms and an expert manual interpretation are incorporated into geomechanics models. These geomechanics models are intended to issue recommendations regarding wellbore instability while drilling or hydraulic fracturing job design. The comparison between the results of each model are then examined against the outcome of each job in the field. The MEM produced from the algorithms shows higher accuracy when predicting wellbore instabilities and breakdown in hydraulic fracturing job.
In addition to improving the accuracy of different geomechanics models, the novel method presented in this work also creates a pathway to incorporating information from image logs into sophisticated numerical modeling solutions. It is shown that three-dimensional (3D) geometries interpreted these logs can be incorporated into 3D meshes for the purpose numerical modeling of wellbores through methods like the finite element model.
A Data-Driven Model for Bound Fluid Prediction in Carbonate Reservoirs
Authors: Alshuwaikhat Ali , Albalawi Ghadah , Alshammasi Laila , Meridji Yacine 
Published: March 2023

Abstract
The objective of this paper is to develop a machine learning workflow in order to replicate the Nuclear Magnetic Resonance (NMR) bound fluid volumes utilizing triple-combo data. The developed model is to overcome the logging operations challenges and to enhance the petrophysical formation evaluation in carbonate reservoirs and most importantly a proper completion design to avoid free water bearing zones (Dodge et al 1998).
The presented data-driven model is based on artificial intelligence (AI) and data mining techniques. In order to select the required data input, many logging parameters were investigated and several iterations with different sets of parameters were attempted to identify those with the most impact on bound fluid volume prediction. Henceforth, the iteration with the lowest root mean square error (RMSE) is to be selected. The model was trained, calibrated and validated to accurately predict the NMR bound fluid volume. The main purpose of this study is to draw attention to the capabilities of the artificial intelligence techniques to provide logging responses with excellent match compared to the measured logs in the event that operational complexities or budget constraints arise.
Several wells with excellent data quality were selected from the same field to build and test the RF (Random Forest) model. 56% of the wells were used for training and the remaining 44% were kept for blind tests. During the modeling process, several iterations were attempted to identify what was really driving the BFV prediction, the best model was chosen based on the lowest RMSE (Root Mean Square Error). The model statistics indicated the combination of resistivity, density, neutron, GR, produced the best model with lowest RMSE = 0.78 pu (porosity units). The model was later deployed to the remaining blind test wells, the results were of excellent accuracy when compared to the measured log data with error difference way below the acceptable industry standard of 1 pu. With accurate BFV prediction, free water zones can now be identified and avoided during completion. This methodology will help operators reduce risk, maximize production without straining their budget to acquire NMR logs in every horizontal development well, or even attempt formation tester samples unnecessarily.
Implementing this new machine learning methodology will help in increasing the confidence in petrophysical analysis and reservoir management decisions, as well as optimizing the logging runs. This results in cost savings without compromising the quality of the formation evaluation, in addition to minimizing the carbon footprint associated with unnecessary operations.
Fully Coupled Hydromechanical Approach for Flow in Fractured Rocks Using Darcy-Brinkman-Biot
Authors: He Xupeng , Zhang Zhen , AlSinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: March 2023

Abstract
Coupling flow with geomechanical processes at the pore scale in fractured rocks is essential in understanding the macroscopic fluid flow processes of interest, such as geothermal energy extraction, CO2 sequestration, and hydrocarbon production from naturally and hydraulically fractured reservoirs. To investigate the microscopic (pore-scale) phenomena, we present a fully coupled mathematical formulation of fluid flow and geomechanical deformation to model the fluid flow in fractured rocks. In this work, we employ a Darcy-Brinkman-Biot approach to describe the fully coupled flow and geomechanical processes in fractured rocks at the pore scale. Darcy-Brinkman-Stokes (DBS) model is used to model multi-scale flow in the fractured rocks, in which fracture flow is described by Navier-Stokes equations and flow in the surrounding matrix is modeled by Darcy's law. With this approach, a unified conservation equation for flow in both media (fracture and matrix) is applied. We then apply Biot's poroelasticity theory and Terzaghi's effective stress theory to capture the geomechanical deformation. The continuity of the fluid pressure is imposed to connect the DBS equation and the stress-seepage equation. This coupled model is employed to determine the permeability within the microfracture. Numerical results show that this coupled approach can capture the permeability under the effects of solid deformation and multi-scale formation. We develop a fully coupled model to capture the pore-scale flow-geomechanically process in fractured rocks. To our knowledge, the fully coupled framework is developed and applied to characterize fracture permeability at the pore scale in fractured rocks for the first time.
Optimization Model of Fracture Parameters of Shale Gas Fracturing Horizontal Well Based on Genetic Algorithm
Authors: Zhao Xurong , Lan Tian , Tang Zekai , Mu Lingyu , Hu Lianbo , Song Zhigang , Chen Zhiming , Zhou Yicheng 
Published: March 2023

Abstract
Based on a genetic algorithm and field production data, this paper reasonably optimizes the fracture parameters of multistage fractured horizontal wells (MFHWs). First, a mathematical model of MFHWs in shale gas reservoir is proposed under the conditions of variable production and variable pressure. Then, the main factors affecting well production are determined by sensitive analysis. Finally, the production prediction model based on a genetic algorithm (GA) is used to optimize the fracture parameters of the Fuling shale gas reservoir. The results show that in the study area, the number of fractures in typical wells is 10, and the fracture half-length is 90m, and the fracture conductivity is 15.24mD.m. The effective fracture half-length is the main parameter affecting production, so the effective fracture half-length and effective fracture number should be increased reasonably, and the support concentration should be increased properly. What's more, the optimal solution of fracture half-length is 160m, and the optimal solution of fracture number is 8, and the optimal solution of fracture conductivity is 20 mD·m. This paper provides a new idea based a genetic algorithm for optimizing fracture parameters of shale gas wells in the Fuling area.
From Well to Field: Reservoir Rock Porosity Prediction from Advanced Mud Gas Data Using Machine Learning Methodology
Authors: Anifowose Fatai , Mezghani Mokhles , Badawood Saleh , Ismail Javed 
Published: March 2023

Abstract
The utility of advanced mud gas (AMG) data has been limited to fluid typing and petrophysical correlations. There is the need to extend the utility to real-time reservoir characterization prior to wireline logging and geological core description. Our first attempt to predict reservoir rock porosity within a well yielded good result. This study improves on the previous effort by utilizing big data obtained from combining various wells in the study area.
We used machine learning (ML) methodology in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 20,000 data points collected from representative wells were used to prove the concept of predicting the porosity in an interval or section of any well within the study area. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The combined dataset was randomly split into training and validation subsets in 70:30 ratio. The 30% validation subset simulates a missing well interval or section.
Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model outperformed the others. The RF model gave a training and validation correlation coefficient (R-Squared) values of 0.94 and 0.83 respectively compared to 0.36 and 0.35 for the ANN and 0.84 and 0.73 for the DT models respectively. However, the p-value and mean errors show that the models are statistically acceptable. Having showed in a previous research that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. We conclude that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research.
This study has confirmed the feasibility of predicting porosity based on a dataset of combined wells and the huge benefit in extending the utility of AMG data beyond the traditional workflows. This approach is capable of complementing existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.