bluuebunny/crossref_metadata_2025 huggingface dataset:
Enabling Synthetic PLT Through Integration of Brittleness Indicator and Porosity-Independent Permeability to Improve Understanding of Expected Flow Profile in an Unconventional Reservoir: Case Study from Kuwait
Authors: Abdulkarim Anar , Abd El-Aziz Sabry
Published: October 2022

Mauddud carbonates in the Bahrah Field of Kuwait, with their low permeability, are often considered as unconventional reservoirs, which require multi-stage hydraulic fracturing stimulation. Optimal stage distribution is one of the key elements in this type of completion design. This paper presents the forecasting methodology for estimating the production performance of each stage which can later help to optimize stage placement in long horizontal sections.

High-resolution micro-resistivity imaging and an azimuthal sonic tool, in combination with conventional triple-combo logs and formation pressure testing, were run in a 6.125-in. section to help develop an effective and sustainable production plan, based on the petrophysical and geomechanical properties of the heterogeneous tight carbonate reservoir. The use of image data helped to evaluate the permeability distribution close to the borehole wall more accurately than conventional porosity-based permeability relationships, which showed marginal correlation with actual permeability. Sonic and density logs helped to estimate rock mechanical properties. Mobilities derived from the formation pressure build-up curve were used to normalize permeability indicators.

The porosity-independent image permeability indicator, which was developed specifically for carbonate formations and is based on the electrical conductivity distribution around the circumference of the borehole, showed fair correlation with the brittleness indicator. The brittleness indicator, on the other hand, exhibited a high degree of correlation in all completion stages with the production logging (PLT) data. The good correlation of production information with the brittleness indicator, derived from Poisson's ratio and Young's modulus, is in line with the main inputs for hydraulic fracture design, which use these mechanical properties. Both brittleness and image permeability indicators were used to improve well placement in the sweet part of the reservoir and played an important role in the success of this complex process. This process also enables the potential for synthetic PLT derivation, which can help to improve understanding of the expected flow profiles from different frac stages.

The case study presented in this paper demonstrates the promising benefits of integration of open-hole logs to derive synthetic PLT data that can improve completion design and enhance production forecasting in an unconventional reservoir. The availability of several years of production and formation pressure dynamics data also helped to extend the application of the methodology to forecast future production stability for the field.
Sequence Stratigraphy and Paleobathymetry of the Lower-Middle Miocene Succession, Gulf of Suez, Egypt
Authors: Kelany Abdel Rahman M. , Farouk Sherif F. , Kassem Ahmed A. , Abuel Majd Mohamed E.
Published: October 2022

In the Gulf of Suez rift basin, the Miocene sequence is a significant hydrocarbon resource. An integration of the planktonic foraminifera, and benthic foraminiferal biofacies are data used to build a detailed and fine chronostratigraphic scheme for the lower part to middle part Miocene succession in the October Field, Gulf of Suez. The paleoecologic habits and paleobathymetry of benthic foraminiferal biofacies, as well as the planktic/benthic (P/B) ratios of the studied successions, are used to determine the paleoenvironmental conditions that are in effect during the deposition of the studied Miocene successions. Elphidium biofacies are representative of the inner shelf paleoenvironment, Nonion biofacies and Siphonina/Cibicides biofacies are representative of the middle shelf paleoenvironment, Bulimina and Uvigerina biofacies are representative of the paleoenvironment outer shelf. The paleoenvironmental interpretations and chronostratigraphic for data and the nature of stratigraphic relations are integrated to distinguish five depositional sequences in the studied section. These are two in the Aquitanian/Burdigalian, one in the Burdigalian-Langhian, one in the Langhian, and one sequence in the Serravallian ages. The sequence boundaries were marked by subaerial exposures and hiatuses. A detailed correlation between the distinguished sequence stratigraphic lower-middle Miocene frame and those established in and outside Egypt is attempted. The absolute time equivalents of the planktic foraminiferal zones are adjusted by comparison with the geologic time scale of Gradstein et al. 2022. This comparison points out a more or less compatible correlation, with a few deviations that may be related to tectonic events that led to the absence of some recorded depositional sequences.
State-Of-The-Art of Carbonate Pore Type and Permeability Modelling: An Approach of Petro-Acoustic Modelling by Using Lucia Rock Fabric Number (RFN) in Central Luconia, Offshore Sarawak
Authors: Ling Kuok Hsu , Zulkiply Hapiz
Published: October 2022

Central Luconia province is located in South China Sea, Sarawak with development of more than 250 carbonate pinnacles and platform in Middle Miocene to Late Miocene. The study proposes a methodology that has been successfully applied and calibrated to 20 exploration or appraisal wells in both carbonate pinnacles and platform with the objective to model pore types from compressional velocity and subsequently used it for permeability prediction based on Lucia's rock fabric number (RFN). Porosity, permeability, petrography and mercury injection pressure (MICP) data of more than 2000 core plugs and sidewall core samples were analyzed to establish functions in between compressional velocity, pore types and permeability.

This paper is established based on two concepts by many authors in the past, but no publication to date has linked these two concepts together. The first concept is the poroelastic behavior of carbonate rock under constant pressure and 100% water saturation is controlled primarily by a combination of porosity and pore geometry (Wyllie et. al., 1958; Anselmetti & Eberli, 1993; Anselmetti et. al. 1997; Eberli et. al., 2003). High aspect ratio pores sample such as molds and vugs, typically has higher pore throat size. The sonic velocity of this sample will plot above Wyllie-time-average (WTA) equation. Low aspect ratio pores such as interparticle and intercrystalline pores, typically has lower pore throat size. The sonic velocity of this sample will plot below WTA equation. The second concept is the Lucia rock fabric number (RFN) which described porosity – permeability relationships for various particle- size groups in non-vuggy or interparticle porosity rocks only. However, our MICP dataset conclude that Lucia RFN number is inversely proportional to dominant pore throat size in carbonate rock with little to no presence of primary porosity. The dominant pore throat size was determined based on the apex of curve in the crossplot of mercury saturation divided by injection pressure to mercury saturation.
Carbonate Sedimentary Facies Characteristics and Model of the Lower Cretaceous Thamama Group in the United Arab Emirates
Authors: Deng Anxiong , Grover Anurag , Gao Xiaoli , Al Mesmari Abrar , Wang Ailing , Ahmed Jahan Zeb , Lv Lu , Al Shamsi Saif Ghanim , Zhao Wenhao
Published: October 2022

The purpose this study is to use geophysical technology, analyze the characteristics of typical sedimentary facies in the study area, extract sensitive seismic attributes, compile sedimentary facies distributions, establish a sedimentary model, and improve the understanding of the distribution law of complex carbonate reservoir in oil and gas exploration and development. The Cretaceous Arabian Basin is a typical gentle slope carbonate basin with rich oil and gas resources. The main reservoirs in the study area have complex lithology, including bioclastic grainstone, bioclastic packstone and rudstone. However, the heterogeneity of carbonate rocks is strong and the sedimentary facies change rapidly, which increases the difficulty of understanding the distribution law of reservoirs.

Based on core data, well logs, seismic data and well-to-seismic integration, the sedimetary facies study of the Cretaceous Thamama Group has been completed. Typical lithology and logging facies were identified. The logging facies were established, multi-well facies were carried out; and sedimentary characteristics were analyzed. Multi-attributes have been analyzed including amplitude, frequency, continuity and phase attributes to extract sedimentary facies maps by combining lithofacies and logging facies. Based on the above analysis results, a sedimentary model has been summarized.

According to this study, the target strata are mainly carbonate deposits of gentle slope with shoal, inner shoal and grain flat facies deposits. Four typical logging facies were identified consisting of low energy shoal, margin shoal, grain shoal and grain flat microfacies. The favorable reservoirs are mainly bioclastic grainstone and packstone formed in a medium-strong hydrodynamic environment. The sedimentary model of the study area is established to analyze the distribution of target reservoirs and guide the analysis of favorable reservoirs in the area. The Thamama Formation has stable thickness in general, the depositional environments changed from subtidal to inner shoal of restricted platform, to grain shoal and grain flat. It is concluded that the Shuaiba Formation and the Kharaib Formation of the Lower Cretaceous Thamama Group in the middle of the study area are the most favorable reservoir development formations, and the central and northern areas are the most favorable oil and gas accumulation areas.

Through this study, a more complete and instructive carbonate slope sedimentary model has been established. In addition, through the multi-attribute analysis technology and optimization method, we have completed and deepened the understanding of the distribution of the target layer sedimentary facies, and provided a new geophysical comprehensive research method. However, due to the complexity of carbonate rocks, further research is needed.
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

Objectives/Scope

The 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, Process

Modern 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, Conclusions

The 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 Information

For 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

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

Drilling 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

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

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

A 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

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

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

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

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.
Changing the Game of Unpiggable Pipeline Integrity: The Petronas Ultra Long Range Acoustic Technology (ORCA™)
Authors: M Ali M Nazmi , M Tuselim Ahmad Sirwan , Abu Bakar M Hisham , A.Kalam Azad M Fithri Azad , Azami M Fadhli , Ab Rahim Lukman , Hashmani Manzoor Ahmed , May Zazilah , A Rahman Noor A'in , Mahmud Muhammad Shazwan , Hashim Mohammad Firdaus
Published: October 2022

This technology is created and developed by exploring and utilizing the capability of the passive acoustic-based which design intended to detect corrosion at unpiggable onshore pipeline without major modification or disrupting the operation. ORCA™ technology consists of the capabilities and functionalities to perform analysis and assessment utilizing real-time and historical data to detect the corrosion behavior over the period. If these corrosions are injurious in nature, further response either immediate or schedule can be executed to prevent pipeline failure.

The passive, low acoustic-based sensor installed at 2 ends of the pipeline which communicate through Data Acquisition System (DAQ) which subsequently transmit data via public mobile network linking through virtual private network, wide area network which transferred data storage to the digital server. A specialized analysis software capable of performing data pre-processing, cleansing, AE features identification & sizing with Deep Learning capability & post processing with determination of risk and integrity to facilitate system real time capability are also discussed in this technical paper. Acoustic Emission transducers installed between the test section and software to collect and filter the data.

The technology has been deployed on one Feed Condensate pipeline back in 2020/21 with continuous data collection. Higher accuracy was achieved comparing against latest intelligent pigging (IP) data. The validation has been conducted by using Mean Absolute Error (MAE) method and API 1163 matrix and this higher accuracy level has been obtained through the maturity of deep learning in adapting with the environmental and process conditions.

ORCA™ has been approved for replication on PETRONAS 18 nos. of onshore unpiggable pipelines from 2022-2026. The positive value creation is estimated for long term application by 20% OPEX reduction over the life cycle. The technology will help to eliminate the occurrence of pipeline failure due to improper integrity management.

In conclusion, ORCA™ will change the way of managing the integrity of unpigable pipeline as previously ICDA & ECDA approached has been used in estimating the integrity condition of the pipeline.
Nonlinear Anti-Vibration Compliant Mechanism with Inertia Amplification
Authors: Feng Xiao , Liu Tianhu , An Ertai , Feng Jian , Qi Long
Published: October 2022

This paper presents a novel nonlinear anti-vibration compliant mechanism with inertia amplification (NAVCM-IA) for low frequency vibration isolation. This compliant mechanism consists of three-dimensional helical rods with stiffness nonlinearities, and an attached horizontally-installed inertia disc which performs as an inertia amplification. With this unique design, beneficial nonlinear stiffness can be achieved, which reduce the dynamic stiffness at equilibrium position and improve the vibration isolation performance at low frequency range. Moreover, the inertia amplification driven by the compliant mechanism achieves an apparent high dynamic mass and, accordingly, a low resonance frequency. The nonlinear compliant mechanism with inertia amplification is validated by experimental prototypes for their special nonlinear features. The proposed NAVCM-IA is a unique passive and adjustable low frequency anti-vibration solution in extensive engineering applications.
A qualitative study of the psychological status of clinical nursing trainees in hotel isolation in the context of a novel coronavirus pneumonia outbreak
Authors: Wen Yu , Xin Hongjie
Published: October 2022

Objective To understand the psychological state of nursing students in hotel isolation under the new crown epidemic.
Methods Using semi-structured in-depth interviews, 23 nursing students in hotel isolation were interviewed.
Results Six themes were extracted: personal - sunshine is always after the storm; others - big love without discrimination; family - warm harbour; school - warmth outside the epidemic; hotel - service with a smile, fighting the epidemic together; society - safe distance is possible, but warmth and care are not.
Conclusion Female students are more prone to anxiety than male students when facing isolation in hotels; the psychological stress of unmarried nursing students is lower than that of married nursing students; the psychological state of postgraduate students is better than that of undergraduate students when facing isolation in hotels; a good psychological state of nursing students when facing isolation in hotels requires the joint efforts of individuals, others, families, schools, hotels and society.
Morphological response and tumor shrinkage as predictive factors in metastatic colorectal cancer treated with first-line capecitabine, oxaliplatin, and bevacizumab
Authors: Nagaoka Tomoyuki , Osumi Hiroki , Ueno Teruko , Ooki Akira , Wakatsuki Takeru , Nakayama Izuma , Ogura Mariko , Takahari Daisuke , Chin Keisho , Matsueda Kiyoshi , Yamaguchi Kensei , Shinozaki Eiji
Published: October 2022

Purpose Morphologic response (MR) is a novel predictor of chemotherapeutic efficacy in solid tumors, especially those treated with anti-vascular endothelial growth factor (VEGF) antibodies. Nevertheless, the importance of MR in systemic chemotherapy for colorectal liver metastases (CLM) remains unclear. This study aimed to evaluate the usefulness of MR as a factor associated with the therapeutic effects of chemotherapy plus bevacizumab for initially unresectable CLM.
Methods We retrospectively evaluated the associations among MR and/or Response Evaluation Criteria in Solid Tumors(RECIST), progression-free survival (PFS), and overall survival (OS) in patients who received first-line capecitabine, oxaliplatin, and bevacizumab treatment for initially unresectable CLM using multivariate analysis. Patients who showed a complete or partial response based on the RECIST, or an optimal response based on MR, were defined as “responders.”
Results A total of 92 patients were examined, including 31 (33%) patients who showed an optimal response. PFS and OS estimates were comparable in MR responders and non-responders (13.6 vs 11.6 months, p=0.47; 26.6 vs 24.6 months, p=0.21, respectively). RECIST responders showed better PFS and OS than non-responders (14.8 vs 8.6 months, p<0.01; 30.7 vs 17.8 months, p<0.01, respectively). The median PFS and OS estimates of MR and RECIST responders were better than those of single responders or non-responders (p<0.01). Histological type and RECIST response were independently associated with PFS and OS.
Conclusion MR predicts neither PFS nor OS; nevertheless, it may be useful when combined with the RECIST.
Approval by the Ethics Committee of The Cancer Institute Hospital of JFCR in 2017 (No. 2017-GA-1123): retrospectively registered
Research on online monitoring and early warning system of transmission line galloping based on multi-source data
Authors: Dong Kai , Shen Qingbin , Wang Chengyi , Dong Yanwu , Lu Ziqiang
Published: October 2022

When the conductor is covered with ice on a non-circular section, and there is low-frequency, large-amplitude motion under wind excitation, it is usually called the transmission line galloping phenomenon. Due to the large amplitude, the galloping of the transmission line will lead to line fatigue, increase in tension, damage to hardware, or toppling of towers. In addition, it will also lead to flashover, tripping, and other transmission accidents caused by the short phase distance, which is not conducive to the safe operation of power grids. In order to improve the safety of power grid operation, this paper designed an online monitoring and early warning system for transmission line galloping on the basis of multi-source data and verified the system through implementation. It was found that the safety factor was 1.568, which determines its early warning level and affirms the feasibility of this study. This research has a positive role in improving the safe operation of the power grid.
From description to code: a method to predict maintenance codes from maintainer descriptions
Authors: Anand Srini , Keefer Rob
Published: October 2022

Aircraft maintenance crews enter the actions performed, the time required to complete the actions, and process followed to complete the action into a system of record that may be used to support future important operational decisions such as part inventory and staffing levels. Unfortunately, the actions performed by maintainers may not align with structured, predetermined codes for such actions. This discrepancy combined with an overabundance of structured codes has led to incorrect and polluted maintenance data that cannot be used in decision making. Typically, the unstructured textual fields accurately record the maintenance action, but are inaccessible to common reporting approaches. The textual fields can be used to cleanse the structured fields, thereby making more data available to support operational decision making. This paper introduces a natural language processing pipeline to predict C-17 US Air Force maintenance codes from an unstructured, shorthand text record. This research aims to cleanse problematic structured fields for further use in operational efficiency and asset reliability measures. Novel use of text processing, extraction, clustering, and classification approaches was employed to develop a natural language processing pipeline suited to the peculiarities of short, jargon-based text. The pipeline evaluates the frequency of structured field values within the datase and selects an appropriate machine learning model to optimize the predictive accuracy. Three different predictive methods were investigated to determine an optimal approach: a Logistic Regression Classifier, a Random Forrest Classifier, and Unsupervised techniques. This pipeline predicted structured fields with an average accuracy of 93 % across the five maintenance codes.
Специфика структурной организации метакогнитивных компонентов рефлексивности
Authors: Карпов Анатолий В. , Карпов Александр А. , Чемякина Анна В.
Published: October 2022

Введение. В настоящее время особую актуальность приобретает исследование когнитивных и метакогнитивных процессов в профессиональной деятельности. В плане решения этой стратегической задачи объективно необходима конвергенция исследований в двух важных направлениях – в психологии рефлексии и метакогнитивизме. Новизна исследования состоит в том, что в нём выявлены и проинтерпретированы базовые особенности и закономерности, а также операционные средства структурной организации рефлексии и эксплицирующие ее несводимость к аддитивной совокупности входящих в нее основных метакогнитивных процессов. Методы. Выборку (n = 220) составили представители основных классов деятельности – субъект-объектного, субъект-субъектного и субъектно-информационного, а также студенты вузов Ярославля и Москвы. Психодиагностика выполнена с помощью авторских методик диагностики рефлексивности (А. В. Карпов, В. В. Пономарева) и метамышления (А. А. Карпов), а также комплекса разработанных в метакогнитивизме методик – методики Р. Диксона – Д. Халтча (Metamemory in Adult – MIA), методики Д. Эверсон для диагностики метапланирования, методики диагностики мотивационных метакогнитивных стратегий (MSLQ) и др. Результаты. Установлено, что индивидуальная мера рефлексивности не тождественна величине метакогнитивного потенциала, образованного аддитивной совокупностью основных метакогнитивных процессов и качеств. Следовательно, в ней как интегральном индивидуальном качестве имеет место действие специфически системных закономерностей и механизмов собственно интегративного типа, порождающих синергетические эффекты и приводящих к генерации нового – специфического для нее содержания. Обсуждение результатов. Результаты проинтерпретированы с позиций основных положений метакогнитивизма, а также базовых положений теории систем и психологии рефлексии. В заключение сделан вывод о несводимости содержания рефлексивности к аддитивной совокупности входящих в нее парциальных составляющих – метакогнитивных процессов и качеств, что определяет специфичность ее психологического статуса и ее своеобразие как интегрального психического свойства.
Регуляторные ресурсы увлеченности работой и преодоления профессиональных деформаций в контексте проблемы благополучия человека в рабочей среде
Authors: Кондратюк Наиля Г. , Моросанова Варвара И.
Published: October 2022

Введение. Статья посвящена исследованию регуляторных ресурсов профессионального благополучия человека. Теоретически обосновано, что оно может быть охарактеризовано увлеченностью работой и выраженностью профессиональных деформаций. Осознанная саморегуляция рассматривается как метауровень целостной системы психической саморегуляции, включающий когнитивно-регуляторные и личностно-регуляторные ресурсы достижения поставленных целей. Эти ресурсы способствуют решению широкого круга задач в различных видах активности человека. Методы. Выборка: 119 профессионалов в возрасте от 19 до 54 лет. Методики: опросник «Стиль саморегуляции поведения – ССПМ 2020», русскоязычный вариант «Утрехтской шкалы увлеченности работой, UWES», шкала «Личностные и поведенческие деформации» комплексной диагностико-превентивной системы «Комплексная диагностика и коррекция стресса, ИДИКС». Результаты. С использованием методов структурного моделирования показано, что чем более развита осознанная саморегуляция достижения целей, тем выше увлеченность человека своей работой и меньше вероятность появления у него профессиональных деформаций. Показано, что личностно-регуляторные параметры саморегуляции (надежность и гибкость) не только являются предикторами увлеченности работой и преодоления профессиональных деформаций, но и выступают медиаторами влияния на них когнитивно-регуляторных свойств личности. Таким образом, развитие осознанной саморегуляции достижения целей может служить регуляторным ресурсом увлеченности работой, преодоления профессиональных деформаций и достижения психологического благополучия человека в рабочей среде. Обсуждение результатов. Полученные данные раскрывают перспективы ресурсного подхода к изучению осознанной саморегуляции для решения проблем благополучия человека в различных средах и могут быть использованы при разработке программ поддержки психического здоровья и благополучия человека в профессиональной среде.