bluuebunny/crossref_metadata_2025_split huggingface dataset:
Foredeep Prospectivity in a Compressional Tectonic Regime, the Pabdeh Group, Ras Al Khaimah
Authors: Swire Paul H , Firpo Giacomo , Hibbert Michael , Burckhart Thibaut 
Published: October 2022

AbstractObjectives/ScopeThe Pabdeh Group of Tertiary Paleocene to Oligocene age was deposited in a foredeep basin created by an obducted compressional thrust front that extends from Northwestern Oman into the Northern Emirates. The unit is represented by marls interbedded by thin calciturbidites and sandstones, the latter of which become more frequent in the Oligocene. The maximum thickness of the Pabdeh in the basin depocentre is ~3.5 km. The hydrocarbon play is proven in the Hamidiyah oilfield, located in Ajman, and is being pursued in Ras Al Khaimah utilizing recently acquired 3D seismic and legacy well data.Methods, Procedures, ProcessModern Play Based Exploration techniques including interpretation and integration of 3D seismic, Full Tensor Gravity (FTG), local well information, integration of biostratigraphic data, and geochemical analysis of source rocks was used to delineate the Pabdeh play area. The identification of delta fronts, channels, and basinal fans on seismic was linked to the complex interplay between tectonic and eustatic base level changes. Seismic morphologies were then supplemented by seismic amplitude analyses, and outcrop studies in the Emirate of Ras Al Khaimah (RAK). Ductile compressional folding due to closure of the Neo Tethys Basin is proven to have also effected sedimentation patterns with ponded fans also developing on the paleo-shelf.Results, Observations, ConclusionsThe Pabdeh Group is a thick monotonous unit that requires high resolution biostratigraphy with seismic guided sequence stratigraphy, to identify mappable units and unconformities across the foredeep basin. The Middle Eocene Pabdeh Fm. Is the primary reservoir target in the Hamidiyah Field. Pabdeh calciturbidites represent lowstand sedimentation basinward of major incisions. These calciturbidites were deposited as debris flows originating from deltas near the thrust front. Submarine channels were cut into the shelf edge along the compressional thrusting front (in the present near offshore of RAK), and basinal fans can be observed in the present-day offshore 3D seismic. The calciturbidite facies has been identified in several wells with clear hydrocarbon shows and successful tests. Structural traps can be defined in tight ductile folds with a N-S trend, along with potential for stratigraphic traps. Main risk factors are around reservoir effectiveness and clearly defined migration pathways.Novel/Additive InformationFor the first time the detailed sequence stratigraphy of the Pabdeh Group has been used to determine prospectivity in RAK and is now being expanded across the Northern Emirates using modern 3D seismic and Play Based Exploration (PBE) techniques.
Dehydration of Rocks as the Main Source of Hydrocarbons and Diamond-Bearing Structures within the Earth's Crust in Various Regions of the Earth
Authors: Harutyunyan Albert V. 
Published: October 2022

Abstract
Annotation. The genesis of hydrocarbons has been debated for more than 300 years and continues to the present. The discussion of the problem led to the formation of organic and inorganic scientific schools. Over time, the hypothesis of polygenesis was also formed. With the development of engineering and technology, new concepts on the genesis of hydrocarbons and diamond-bearing structures were presented. One of these is the concept presented by us, according to which hydrocarbons and diamonds are formed not only at great depths of the mantle, but also at different depths of the Earth's crust in different regions of the Earth, due to the dehydration of serpentinized rocks. Dehydration of rocks occurs in both oceanic and continental crust. Under the continental slope, due to the collision of the continental and oceanic crust, the dehydration of serpentinized rocks of the 3rd layer of the oceanic crust occurs. Dehydration of rocks also occurs at various depths of the continental crust. Formed hydrocarbons and geofluids migrate to the upper horizons of the crust, differentiate and accumulate in fractured granites and sedimentary layers. Based on the proposed concept, the genesis of some giant deposits of the Earth, the Gulf of Mexico, the Caspian Depression, and Western Siberia is presented. According to laboratory studies, dehydration of rocks in the earth's crust causes ultra-high pressures. Kimberlites and explosive tubes are formed from carbon-containing components present in the medium. The proposed concept is characterized by more than 17 criteria that are set before prospecting and exploration in different regions of the Earth. The results obtained cover a wide range of issues of geology, geophysics and seismology. The results are presented to specialists for wide discussion. Further research is presented to the author in close cooperation with specialists from these fields of science from around the world.
Ensemble Machine Learning Model for Predicting Rock Drillability Rate for Composite Lithology
Authors: Gamal Hany , Alsaihati Ahmed , Ziadat Wael , Abdulhamid Mahmoud Ahmed , Elkatatny Salaheldin 
Published: October 2022

AbstractDrilling operations for oil and gas wells are considered one of the highest operating costs for the petroleum industry, and hence, implementing the new technology-based systems is highly required for cost reduction and efficient functionality performance for the drilling system. This paper introduces a successful application of machine learning to develop a drillability rate prediction model while drilling complex lithologies by employing the ensemble technique.The study presents a procedural methodology for developing the machine learning model using three learners named functional network (FN), radial basis function (RBF), and support vector machines (SVM) that include two base learners (FN, and RBF) and one for meta strong learners (SVM). Two data sets were utilized from two vertical wells within the same field that penetrated the same sequence geology of drilled formations that covered anhydrite, carbonates, and abrasive sandstone that are commonly interbedded with silt, mudstone, and shale layers. One data set was used for training and testing the models (5000 data points), while the models were validated through an unseen data set. The data features include real-time sensor data along with conventional log data for predicting the penetration rate across the composite geology sections.The obtained results showed that developing an ensemble model (SVM-Meta) boosted the accuracy performance for predicting the rock drillability rate through the testing and validation stages. The correlation coefficient showed higher than 0.95 for the ensembled model while it has a maximum of 0.91 for base learners during the validation phase. The ensemble machine learning technique succeeded to overcome one of the challenges for predicting the rate of penetration for drilling complex geology formations.
Field Deployment and Validation of Novel Wellbore Strengthening Design Criteria and Enhanced Fracture Gradient in a Depleted Reservoir in Malay Basin
Authors: Tan Chee Phuat , Razak M Solehuddin , Zakaria Siti Shahara , Abitalhah Abiabhar , Wan Mohd Zainudin Wan Nur Safawati 
Published: October 2022

Abstract
Fracture gradient enhancement technology has been developed for drilling mud compounds based on comprehensive large scale laboratory test data which can widen the stable mud weight window for safe and efficient drilling in depleted reservoirs. Based on the laboratory test data, correlations were developed between optimum compound particle size against Young's modulus of the rock and generated fracture width for maximising the wellbore strengthening performance of the compounds. In addition, Enhanced Fracture Gradient (EFG) concept and criteria were developed for the compounds to widen the stable mud weight window. The niche workflow developed for utilisation of the wellbore strengthening design criteria and guidelines was deployed and validated in the pilot deployment of the optimum compound particle size correlations and Enhanced Fracture Gradient criteria in two wells for drilling through a depleted reservoir in the Malay Basin. The enhanced fracture gradient was predicted and incorporated into the mud weight programme and mud loss contingency plan of the wells. The wells were drilled with 10 ppb of the optimum mud compounds and the concentration was monitored and maintained throughout the drilling. In the first well, the maximum ECD exceeded the in-situ fracture gradient of the depleted reservoir by 1 ppg without any losses. Following TD of the hole section, an openhole leak-off test was conducted which validated the predicted average EFG of 1.8 ppg above the in-situ fracture gradient. The predicted EFG was within 0.2-0.3 ppg from the openhole leak-off test value. In the second well, the depleted reservoir was exposed to 0.6 ppg above the in-situ fracture gradient and no losses was observed.
A Novel Realtime Well Collision Avoidance Monitoring by Definitive Dynamic Surveys and Passive Magnetic Ranging
Authors: ElGizawy Mahmoud , Fraser Mark , Lowdon Ross , Dorey Jamie 
Published: October 2022

Abstract
Drilling wells in close proximity to offset wells always involves a collision risk. This could lead to a catastrophic event like a blowout if collision avoidance monitoring techniques fail. None of the existing collision avoidance techniques today include continuous six-axis real-time definitive dynamic surveys to detect the offset well while drilling. Rather, all techniques rely on stationary surveys taken at predetermined intervals to calculate the projection ahead of the bit and to calculate the separation distance from the nearby well. This leads to added rig time and directional drilling complexity from hole washouts.
Magnetic surveys rely on magnetometer sensors to measure the wellbore's azimuth. External magnetic interference such as that exhibited by the casing of a nearby well has an adverse effect on this measurement. Passive magnetic ranging (PMR) uses the measurements of the rotating magnetometers and accelerometers while drilling in the presence of this interference. A distance and direction to an offset well casing string can be determined by analyzing the raw data taken from the sensors. PMR techniques have previously required close spaced, stationary surveys to provide suitable data for ranging. This method demonstrates that definitive dynamic surveys can provide accurate ranging data while drilling, avoiding the need to stop and take stationary readings.
The selected wells for this study are all drilled from the same platform with a relatively small positional uncertainty in order to have a robust comparison for the new technique's estimates of the position and direction to those same offset wells. The presented study includes six wells in a batch drilling sequence. The PMR was conducted in real time while drilling from the drilling well to the offset well. Results show the successful monitoring of the distance and direction of the offset well. Also, the results are accurate and compare well with the known position of the offset well.
The technique introduced in this paper offers a unique way to avoid colliding with offset wells while drilling. This could be of particular help where the positions of the offset wells have poor accuracy and you can avoid them without the need for time-consuming static measurement while drilling (MWD) surveys. This new collision avoidance technique enables an offset well to be ranged continuously, enabling for magnetic range to the offset well while drilling. Enabling the approach and deviation from an offset well to be continuously monitored helps ensure a safer and more efficient well delivery.
DNN Inversion of Gravity Anomalies for Basement Topography Mapping
Authors: Ashena Zahra , Kabirzadeh Hojjat , Wang Xin , Lee Youngsoo , Woo Ik , Ali Mohammed , Kim Jeong Woo 
Published: October 2022

AbstractA gravity inversion technique using Deep Neural Networks (DNN) was developed to construct the 2D basement topography in offshore Abu Dhabi, UAE. Forward model parameters are set based on the geological features in the study area. Hundreds of thousands of synthetic forward models of the basement and their corresponding gravity anomalies are generated in a relatively short time by applying parallel computing. The simulated data are input to our DNN model which conducts the nonlinear inverse mapping of gravity anomalies to basement topography. To assess the model's robustness against noises, DNN models are retrained using datasets with noise-contaminated gravity data whose performances are evaluated by making predictions on unseen synthetic anomalies. Finally, we employed the DNN inversion model to estimate the basement topography using pseudo gravity anomalies over a profile in offshore UAE.
Synthetic Data Generation for Machine Learning Applications in the Energy Industry
Authors: Alnumay Yazeed , Alrasheed Ali J. , Trigui Hassane , Halawani Abdulwahab , Alshiekh Mohammed , Patel Sahejad 
Published: October 2022

Abstract
With the ubiquity of machine learning technologies, their adoption in the energy industry is bound to become mainstream. However, a major setback of developing innovative modern machine learning technologies is their significant reliance on large labeled datasets during model training and development. Acquiring and labeling such large datasets can be prohibitively expensive as you need to manually capture and label massive amounts of data points depending on the application. Additionally, when dealing with images in the energy industry, it can be hard to capture images due to elevated safety and security standards as well as sensitive restricted areas. Therefore, an emerging technique that is being utilized is by generating synthetic datasets as an alternative to actual field images for training machine learning models. These modern methods of using synthetic datasets are not exclusive to visual data as we can also simulate acoustic and time-series data, among others. However, in this study we will limit our scope to focus on computer vision related applications and generating synthetic images.
We present a discussion of the 3D rendering software that could be used to generate synthetic visual data, such as Blender and Unity, by outlining their respective strengths and weaknesses. Furthermore, a detailed account of common image augmentations is presented to showcase their effects on model accuracy. In order to ensure that models trained on synthetic data perform well when deployed, we validate the model with a relatively small subset of real labeled images.
The difference in accuracy between real and synthetic data will be largely determined by how well the synthetic images represent real images. As such, we show that the simulated images look realistic enough and we expect better transference when predicting real images. To validate our methodology, Convolutional Neural Networks (CNNs) are used to train multiple models for visual inspection using synthetic data generated by the aforementioned 3D rendering software for the following applications: Flange integrityClassifying lever positions (open/closed)
As visual inspections via robotic technologies such as drones increase in facilities, the need for labeled data for machine learning model training increases so as to enable automated asset integrity. We propose a procedure to streamline the model training process by utilizing synthetic images. This will present cost and time reduction as well as the ability to train models for features with sparse real data.
Well Intervention Opportunity Management Using Artificial Intelligence and Machine Learning
Authors: Shekhawat Dushyant Singh , Devgun Vishal , Bhatt Bhartendu , Sinha Rajeev Ranjan , Songchitruksa Praprut , Vadivel Karthick , Dhiman Adarsh , Jain Apurv , Shaha Parimal Unmesh , Holy Ralf 
Published: October 2022

Abstract
Operators face many challenges when selecting well-intervention candidates and evaluating a field’s potential because the process is highly time consuming, labor intensive, and susceptible to cognitive biases. An operator can lose up to USD 10 million/year because of ineffective well-intervention strategies in a single field. The objective of this study is to reduce such losses and standardize the well-intervention process by intelligently using the domain knowledge with artificial-intelligence (AI) and machine-learning (ML) techniques. The workflow developed in this study can automatically and autonomously analyze the surface-subsurface data to expeditiously recommend the top intervention candidates.
The workflow leverages proven petroleum-engineering methods and customizable business logic to identify underperforming wells and then recommend workover techniques, post-workover production, success probability, and profitability. It uses production, petrophysics, reservoir, and economics data to run a series of AI/ML techniques. The data-analytics engine runs k-nearest neighbors to predict post-workover rates, followed by a decision tree to identify the remedies. Artificial neural network, random forest, and Monte-Carlo simulation are adapted to identify new perforation opportunities in existing wells. Analytic hierarchy process ranks the top intervention candidates based on post-workover rate, permeability, remaining reserves, and reservoir-production trends. Finally, Bayesian belief network calculates the probability of success.
With this implementation, the manual benchmarking process of opportunity identification, which usually takes weeks to months, can now be completed within minutes. Once the opportunity is identified and reviewed, it gets registered in the opportunity tracker list for the final evaluation by the asset team. The results are displayed on web-based applications with customizable dashboards and can be integrated with any existing online/offline systems. Because the whole process is now automated and takes very little execution time, petroleum engineers can review the field’s performance on a daily basis. With more than 80% predictive accuracy and 90% time saving compared to the manual process, this workflow presents a step-change in the operator’s well-intervention management capacity. In this paper, the authors discuss the adaptations to the industry-standard AI/ML algorithms and the best practices to provide a faster, more accurate, and efficient well-intervention advisory system.
Smart Energy Analytics for Energy Efficiency Improvement and CAPEX Avoidance
Authors: Sajjad Saqib 
Published: October 2022

Abstract
The energy performance not only directly governs the OPEX but also stimulates the future CAPEX avoidance. Assurance of good energy performance for the processing facilities calls for excellence in all areas encompassing measurement, analysis, actions and reporting. This paper introduces a holistic Smart Energy Analytics to provide an integrated predictive and prescriptive platform for continual energy efficiency improvement. It optimizes operational efficiency by linking the energy performance with the OPEX impact.
The energy performance is focused in multiple set areas. These have been deliberated as modules in the Smart Energy Analytics platform. It includes energy distribution in the form of Sankey diagrams and a real-time energy intensity tracking with a choice of temporal resolution. Energy performance benchmarking across sites and across similar critical equipment is one of the key component of energy analytics and helps in identifying relative gaps and also in sharing the best practices for enhanced performance. A thorough mapping of all energy performance spheres has been embedded in multiple tiers for a structured and a comprehensive smart analytics.
Smart Energy Analytics has provided an integrated solution to address all the energy performance related aspects under one platform. It enhances the capability to identify all sorts of performance gaps and realize them in terms of energy efficiency improvement, energy savings, OPEX reduction, profitability increase and emissions reduction. One core benefit of Smart Energy Analytics is to identify the available spare capacities and leverage them for future expansions, and hence for CAPEX avoidance which is generally overlooked as the energy efficiency improvement benefit. The NPV of CAPEX avoidance potential has generally been observed substantially higher than the NPV of energy efficient operation based OPEX reductions.
There are numerous conventional tools and applications addressing discrete aspects of energy performance. The Smart Energy Analytics is the first of its kind integrated solution which encompasses all the energy performance related areas and aspects. It aims energy efficiency improvement in totality and boosts the capability for opportunities identification.
Unleash the Potential of Upstream Data Using Search, AI and Computer Vision
Authors: Asfoor Hasan Mahdi , Alharbi Dalal Abadi 
Published: October 2022

Abstract
Data is at the heart of Digital Transformation whether it is generated by legacy systems, a word processor, or state-of-the-art sensors. This makes it harder for Upstream professionals to find the right information for operational needs and making decisions. In this work, we describe an approach that utilizes Enterprise Search, AI and Computer Vision to construct a single efficient search layer, called USEARCH, over multiple data repositories. This enables Upstream professionals to perform searches using simple business language and get the information they need wherever it resides.
Our approach to build USEARCH consists of five steps. First, establishing a search infrastructure. Second, indexing content and metadata of documents to make them easily searchable from a single layer regardless of the source repository. Third, utilizing Artificial Intelligence to intelligently tag information within data and documents such as wells, reservoirs and business process labels. Fourth, applying Computer Vision techniques to extract tabular information from documents. Fifth, developing an intuitive user interface to simplify finding data. It can intelligently set the business context based on user domain such as Exploration, Drilling or Petroleum Engineering.
We applied our approach on over 3 million documents of different types such as drilling reports, reservoir studies, well test analysis, biostratigraphy and geological maps and stored on multiple repositories. This results in a massive improvement. Upstream professionals can now find data through a single layer and thus eliminate the overhead of switching searches between repositories. In addition, multiple search queries that used to take minutes have now been replaces by a single query that takes a few seconds and even milliseconds in some cases. Furthermore, search results are displayed in a business context with direct links to data at the source repositories. The application of Computer Vision allows any tabular data within documents to be exported to databases for fast analysis.
Our approach connects all different data repositories and provides user with an intuitive user interface to find data. This unleashes the potential of Upstream data and empowers Upstream analysts to spend more time using data than finding it.