bluuebunny/crossref_metadata_2025 huggingface dataset:
COVID19 Challenges and Opportunities in IT Audit
Authors: Al Hosani Sumaya Sulaiman , Ali Al Hammadi Dr. Eisa
Published: October 2022

During COVID-19 pandemic many projects were managed remotely facing challenges and gaining benefits out of that new way of work. This paper falls under the Project Management category to address COVID-19 challenges and opportunities faced IT auditors and IT auditees during the remote IT audit.

The scope of this paper is to know how IT auditors managed their work, addressing the challenges they faced, and how the oil and gas sector can benefit from conducting virtual IT audit going forward. In addition to that, the paper will cover IT auditees experience with the new audit technique.

The methodology this paper will cover is comparing IT audit preparation, outcome, and auditee responses before and during COVID-19 by conducting interviews with IT auditors and IT auditees within ADNOC group. It will also cover IT audit strategies and techniques that have been applied in order to assure having a successful audit during this critical time.

This year, IT auditors in ADNOC group of companies are still conducting audits virtually. The outcome of this paper would be used as a guidance to support IT auditors in oil and gas sector by:

Sharing challenges and opportunities IT auditors and IT auditees faced. Sharing best practices of virtual IT audit. Sharing lessons learned and provide recommendations on how to avoid addressed challenges.

A virtual audit is a new technique that auditors were forced to deal with, because of COVID-19 pandemic, to perform their tasks. Presenting the challenges and opportunities faced especially in IT audits will benefit the petroleum industry to reduce gaps in this area and benefit from the lessons learned.
How to Build a Digital Twin with Strong Justification & Return of Investment: Case Study from OQ Oman
Authors: Al Maawali Khulood Mohammed , Al Fahdi Khalid Khamis
Published: October 2022

OQ recognizes the significancy of new technologies driving all industries, and in these days alongside with the latest generation of information technologies, including artificial intelligence, machine learning, cloud computing, internet of things and big data, The roles of virtual space are becoming more of a value and the links between the physical and virtual worlds are more intense than ever before. Introducing such development of virtual spaces is allowing for more technologies, and it is well considered an enabler for a proper digital transformation. OQ is an integrated energy company working on oil & gas, where asset reliability and availability are a key aspect to be monitored and maintained. At OQ we admit the fact that this revolution of technology -if implemented in the right way- could result in enhancing the operation, reducing maintenance costs, and increasing profits. This was the driver of the study, which started by reviewing the readiness and needs across OQ brown assets and ended up with a design of Digital Twin (DT) technology with the highest return of investment and strong justification.

The case study summarized the procedure for building a digital twin for OQ brown fields, initiated by assisting OQ assets, highlighting the areas of impact, building strong use cases, selecting the most critical for a pilot project, and designing the project rollout. The procedure was built based on internal and external involvements, internally with different stakeholders inside OQ, and externally with some of the top technology providers of digital twin globally, which allows for proper alignment and for discovering the most recent available solutions and related technologies in the scope of the project. This case study has its value as it is contributing in guiding similar industries in designing their own deployment plan of digital twin considering digital twin strategies to support the consistent and systematic rollout of the technology.
Enhanced Operational Integrity Management Via Digital Twin
Authors: Matthews Stephen John , Sutherland Ross , McIntyre Gareth
Published: October 2022

During the early stages of the Covid pandemic, the oil and gas industry was faced with significant challenges in executing inspections for ageing offshore oil and gas infrastructure together with the management of various operational integrity threats. These challenges were addressed through the collaborative development of remote, digital techniques to minimise inspector time offshore and maximise efficiency.

This initially involved a review and challenge of our existing operational integrity management lifecycle, comprising systems data, risk-based strategy development, inspection planning, inspection execution, data management, anomaly management (including fabric maintenance and engineering repairs) and close-out. Working with our partner, GDi, we sought to drive a step change into digital integrity management, combined with streamlined workflows, activities, and administrative tasks.

The pilot development involved comprehensive laser scanning of a floating production storage and offloading (FPSO) facility to generate a dimensionally accurate digital twin, overlayed with 360° HD photogrammetry to provide a thorough baseline for subsequent general visual inspections of pressure equipment, piping systems and structural elements. General visual inspections can now be executed remotely, without the requirement for an offshore inspector. The digital twin environment also supported transformation of inspector-led activities, through optimisation of processes, digital inspection workflows via tablets and seamless integration with the integrity management platform. The pilot development also involved enhancing the anomaly risk management process, including management of mitigations (such as temporary repairs) and actions required to resolve and close anomalies. For the anomaly actions, the digital twin environment enables the accurate estimation of fabric maintenance scopes and dimensionally accurate repairs for corrective work orders. The system also facilitates a unique overview of cumulative risk via the plotting of anomalies in the digital twin space.

The digitally enhanced operational integrity management system has substantially reduced direct costs and personnel safety risks, enabled substantial improvements in productivity (up to 200% for inspections), and improved the quality of integrity management outcomes.
Scalable Port Inspections Through Underwater, Terrestrial and Aerial Robotic Platforms
Authors: Medagoda Lashika , Galea Mitchell , Bargoti Suchet , Khan Junaid , Dunne Toby , Potiris Steve , Ul Abidin Zain , Anwar Irsa , Jolly Jordan , Windrim Lloyd , Charolia Fazilai
Published: October 2022

Asset management of a marine port or terminal requires inspection of the asset components below the water surface, at the water surface line and above the water surface. In order to determine asset health, high fidelity, multi-modal data is captured remotely using robotics, and subsequently analyzed. This facilitates the production of prioritized actionable insights to be produced. These insights can be scheduled as maintenance works. This paper outlines a novel multimodal inspection system that was deployed at a port to facilitate its remote inspection.

Two inspections are detailed in this paper, with differing inspection requirements and constraints. The inspection operation can be broken down into multiple components, an underwater scan of the port, a water-surface scan, a ground level scan and an aerial scan. The Port Asset required the inspection of the wharf and quay walls. The Marine Terminal Asset required inspection of the complex pipework.

The data collected from the different modalities were represented in the 3D space. The alignment of the different modalities could then be done in this representation. For the Port Asset, where multibeam sonar maps were collected, the difficulty in alignment of the different modalities is apparent due to no shared points below and under-water, which requires estimation using extended features as landmarks. There also exists slight warping between the data that must need localized scaling for certain sections, which may be done using local submaps and scaling. For the Marine Terminal Asset, artificial markers are used to allow scaling of the drone captured photogrammetry. High levels of coverage from the multiple views of the asset from different data collection strategies allowed the complex pipework to be well mapped, with further insights from the co-registered imagery.

The novel information described in this paper is the unique combination of different modalities for a broadscale port inspection, with point clouds from sonar, lidar, photogrammetry and imagery, in a combined reference frame which acts as a digital twin for the port. This also allows unique insights, such as the exact context and magnitude of faults in the port environment, to allow corrective works to be targeted correctly.
Gas Maximization Scenarios Leveraging a Countrywide Integrated Capacity Model and Digitalization of Engineering Workflows
Authors: Hamad Al Selaiti Iman , Mabrook Maged
Published: October 2022

This paper presents the process and results from the applicationof an Integrated Subsurface/Surface Model (Digital Twin) for computing capacity at the country level, or Integrated Capacity Model – ICM, to generate additional revenue through increased gas production from the existing asset facilities.The model is the foundation of the ICM system, a platform that allows ADNOC to evaluate different production scenarios at a field, asset, or country level for operational and planning purposes. The presented case and results are focused specifically on a gas maximization scenario, a very relevant scenario considering the global market tendencies and interest in cleaner provision of energy. The scenario maximizes the gas production of the country while adhering to oil targets and reservoir management guidelines.

Integrated Capacity Model (ICM) is one of ADNOC'sindustry leading technical solution for managing its vast (both in number and size) oil and gas reserves being exploited by thousands of wells and providing stabilized oil and gas to the various export points and processing plants. It is the world's first such large-scale model-based solution. The solution provides several predefined daily runs in an automated manner which can be visualized via an interactive web-based dashboard across the organization. ICM also allows for the configuration of ad-hoc or alternate scenarios to allow for the analysis and evaluation of special operating cases and business plan development. These alternate scenarios address the requirements to evaluate the opportunities and capability of the infrastructure to deliver in exceptional circumstances and for special needs.

This paper presents the configuration details and results of one such scenario. The paper shares the implementation and challenges of maximizing natural gas products across the ADNOCintegrated value chain. This scenario is achieved by setting up a multi-objective optimization function across every asset in theICM model to maximize its gas production while maintaining crude oil targets as required by market and ensuring that well and reservoir management guidelines are honored.

The results of the scenario demonstrate the capability to increase gas production by up to 30% across through the adjustment of the country wide production profile.It is demonstrated that producing a substantial incremental gas volume is possible while honoring any oil targets, particularly in assets with large gas-oil-ratio (GOR) variation. The additional gas availability is achieved through a number of well choke changes, therefore the size of the opportunities in terms of wells and strings can be ranked in such order to balance the practicality with field operations.
Slim Multistage Fracturing—One of the Most-Successful Solutions in the Most Recent 15 Years of China's Multistage Fracturing History
Authors: Zhao Geng , Fould Jeremie , Dong Yifan , Li Changwei , Huo Ying , Liu Xiaoli , Liu Yang , Wang Qiang , Yang Shengfang , Jin Ting , Ma Qian , He Tao
Published: October 2022

Hydrocarbon resources have primarily been accessed from onshore environments. Most of the explored basins have matured, with some even nearing the end of their field lives. Production from these traditional resources has declined over the past few years, and new discoveries of vast resources are becoming rarer.

Global exploration trends focused on deepwater (and even ultradeepwater) fields, deeper onshore formations, and unconventional resources are aligned with the focus of China's hydrocarbon industry. In 2021, China completed its first self-operated subsea deepwater gas project, Lingshui 17-2, and a series of new projects is planned for the next few years. In terms of developing deeper (> 4000 m) onshore formations, Sinopec Northwest set a record in Shunbei Field with an ultradeep oil development that reached 8874.4 m. Famous fields in China for unconventional hydrocarbon development, some of which are in deeper formations, include PetroChina Southwest in the Sichuan Basin, PetroChina Changqing in the Ordos Basin for tight gas/shale gas and shale oil, and Sulige in the Ordos Basin for tight gas. During China's 14th Five-Year Plan period, from 2021 to 2025, the proportion of unconventional oil and gas production will further increase, and production is expected to increase rapidly to align with the country's energy security strategy. Furthermore, the oil and gas industry worldwide has been placing a greater emphasis on carbon footprint reduction; while the number of new oil projects decreases every year, natural-gas projects, especially in deepwater environments and shale/tight reservoirs, have been rapidly increasing.

This paper will focus on the unconventional hydrocarbon development methodology and discuss the evolution of fracturing technology, particularly multistage fracturing completion tools over the past two decades; then, the paper will review the various design concepts, features, and serviceability of representative completion tools during each fracturing period. A successful 10-year tight-gas sandstone application in Ordos, China, will be used to review the slim multistage fracturing sleeve (SMFS), the most-reliable fracturing string design, and its key operational improvements. The design has been informed by previous field knowledge and experiences, resulting in a simple, cost-efficient, and technology perfect adaptability tool that can be used to illustrate the development trend of multistage fracturing completion tools.
Using Ai to Optimize the Use of Gas Lift in Oil Wells
Authors: Docherty David , Erickson Dale , Henderson Scott
Published: October 2022

Optimization of gas lift rates in an oil field with hundreds of wells is a complex challenge. Typically traditional control systems and operating strategies fail to optimize the problem due to this complexity. Typical gas lift optimization challenges in a field include the following: Real-time flow rate prediction of various phasesReal-time wells performanceReal-time pipeline network performance

The dynamic nature of the problem and the variability of the solution space makes it extremely hard for traditional simulation-based solutions to locate the optimal performance point in real-time. The computational requirement is massive and makes it difficult to perform these calculations at the "edge". This is where combining simulations, human expertise, and machine learning technologies such as Deep Reinforcement Learning help build AI that can excel in rapidly computing optimized setpoints in complex domains.

Using pioneering machine teaching methods combined with multiphase simulations this paper will present the solution of using Artificial Intelligence (AI) to optimize gas lift rates in real-time, finding the optimum gas lift rates for a 4 well pad, 200 well system, such that net profit is increased by 5%-25% for different baselines as the reservoir conditions change over field life.
An Innovative In-House Engineering Solution to Manage the Process Alarms
Authors: Lakshminarayan Yogesh , Krishnaprasad Karthik Coimbatore , Tank Yogesh Kumar , Brahmbhatt Hiren Harshadkumar , Al Teneiji Ahmed Mohamed
Published: October 2022

Alarm system plays a pivotal role in the safe management of plant and its machinery. Alarm systems in Process plants are used to identify abnormal situations and to help plant Control room operators take timely, appropriate action. Ineffective Alarm system poses a serious risk to HSE and Plant profitability. The effects can be very serious if these alarm systems do not work well. Many times, alarm system effectiveness is unknowingly undermined by poorly configured alarms which cannot adapt to dynamic conditions. Similar situation was observed in ASAB / SAHIL plant.

In the year 2013, as ASAB/SAHIL commissioned Green field Full field development plant, Control room Operators were overwhelmed with thousands of alarms on Operator screens. ASAB / SAHIL being a hub for SE Fields, it was obligatory to achieve a quick solution of a stable Alarm system. Site Management soon realized the gravity of the situation and an objective was set to have best possible stable Alarm system through innovative, collaborative efforts by an established and dedicated Task force team with a thorough knowledge of the Process.

The Task force team (TFT) comprised of subject matter experts from Maintenance and Operation disciplines. The TFT was led by the Maintenance Manager and the weekly meeting was chaired by Instrument counterpart who were responsible to review, analyze and categorize the Bad actors and notify to all stakeholders of TFT for a resolution. With sheer dedication and diligence work which was performed over a span of 8 to 9 years, finally on April 2021 alarm system in ASAB and SAHIL sites were brought to the Stable state. The sweet success was finally achieved by continuous efforts of TFT members who were focused and determined to see the light at the end of tunnel.
Digitalized Well Head Control Panel Maintenance
Authors: Jayapalan Gopalakrishnan
Published: October 2022

Wellhead control panel ensures safe operation of Oil /Gas well operations. Digitalization of wellhead control panels helps in reducing the downtime. Distributed Control System ( DCS) can be used for collecting the wellhead control panel data and to perform panel health assessment. It triggers maintenance request / short messaging service ( SMS) once it identifies fault / trip. This paper analyses on how digitalized well head control panel maintenance can be carried out and how it reduces well down time considerably.
First Proof of Concept for an Integrated Robot Supervision System
Authors: Audouin Grégoire , Lannes Rémi , Collin Gildas , Bartoli Eric , Munoz Jean-Michel , Potapieff Hugo , Graves Matt , Chevet Guillaume , Paniccia Tommaso
Published: October 2022

TotalEnergies’ use of robotics on remote or offshore sites is considered with the objective of enabling unmanned operation for long periods and as the next frontier for increased personnel safety, industry attractiveness for young talents and further OPEX and CAPEX reduction. TotalEnergies performed several IT/OT system architecture evolutions and developed a specific Robot Supervision System (RSS) equivalent to a fleet management system to help and consolidate know-how in the deployments of specific mobile ground robots.

As the first autonomous and explosion-proof inspection ground robots become commercial, many have difficulties seeing how these new devices can be interfaced with legacy industrial systems and how its data can be represented in a user-friendly interface to make the remote operation of robot efficient & attractive enough. Only this level of interfacing will enable the possibility for automated inspection, operation, and maintenance tasks execution with intuitive programmable interfaces. The robotic devices also need also to comply with detailed specifications in order to communicate its data in real time to the supervision system.

We will describe the specific modifications required to the robot associated systems to be used within a private industrial network and the specification for real time interfacing of its data. Many Robot suppliers initiated their system developments focusing on robot navigation but a lot of the interfacing complexity with operator companies remained to be achieved to exchange data with the site digital twin, the maintenance task planning tool, the cloud machine learning software resources, or the Process Data Management System (PDMS) or even the plant control system (DCS).

The associated functionalities of the RSS will also be presented: Acquisition, historization and user-friendly display of robot's dataIntegrate several robot types or from several vendorsInterface with all company systemsVisual 3D digital twin environment to display data acquired by the robot or useful data for its remote operationIntuitive configuration and management of robot for routine tasks or based on a specific plant event or alarmComply with specific industrial cybersecurity rules

Feedback will also be given on how it has been possible to adapt the IT architectures and how we intend to fit the requirements of OT systems to adopt robots with more critical interactions with the industrial process. We can present our approach for upscaling robot deployment with this on-premises supervision systems capable of historizing the data, generating immersive front-end displays for robots’ operators and managing secured interconnections to corporate systems. Figure 1An experienced operator using the RSS - View of the 2 main dashboard types (summary mashups & 3D view)
Using Autonomous Control to Stabilize Well Performance without the Downhole Pressure Gauge
Authors: Gao Zhuo , Hyder Syed Zeshan
Published: October 2022

Optimizing a gas lift well can be challenging. Essentially, it needs to optimize a complex system, where gas is compressed and injected to the bottom of the well to lift oil/water/gas to the surface. It contains numerous facilities and equipment, from compressor, injection valves, downhole gas lift mandrel, valves, well head, surface separator to the sales lines. Flowing bottom hole pressure plays a critical role in the system. In a lot of cases, stabilizing the flowing bottom hole pressure within a desired range means the system is optimized. However, downhole pressure gauges are expensive, and it isn't realistic to install one on every well, which brings more challenges to optimize gas lift well performance against target flowing bottom hole pressure.

Edge IoT platform provides customized autonomous control logic to accommodate the customer's optimization challenge at the well level. Weatherford deployed the customized autonomous control logic which includes two distinct logics – pressure control logic and injection rate logic. The pressure control logic updates the well head choke diameter based on the calculated FBHP. The injection rate is optimized based on well daily performance.

In the 60 days trial, the field results are proved to be able to utilize the next generation autonomation device to stabilize well head pressure, and casing head pressure, and to minimize buy back gas and reduce the manual intervention.

The autonomous control logic and its corresponding workflow, embedded in the next generation automation device, not only incorporates the industry 4.0 technology, but also leverages both system analysis methods and high frequency real time data to improve the optimization granularity, deliver the values to the customer at the well site.
Utilization of ML to Validate Pressure and Temperature Measurements
Authors: Alquraini Ali Haitham , Al Kadem Mohammad Saeed , Al Ssafwany Ali Radhi
Published: October 2022

The process of validating and monitoring pressure and temperature data is a key element in production engineering as it ensures proper well evaluation. Consequently, wells are frequently surveyed for better reservoir monitoring and accurate measurement of productivity. This study explores a validation method using advanced Artificial Intelligence (AI) and Machine Learning (ML) classification models that were developed utilizing historical data to automatically validate conducted pressure and temperature measurement and communicate observations and alerts to engineers.

The proposed method validates pressure and temperature measurement using ML model based on previously conducted measurement using advanced algorithms. The developed model fed on pre-identified key production and pressure/temperature parameters that are used to classify surveys. Moreover, these parameters were selected based on historical data and measurement reports and then were analyzed and ranked to identify the most important parameters on the performance and accuracy of the model utilizing advance algorithm and correlation analysis. This is to predict and classify test measurement via the utilization of a non-linear relationship through the use of data-based analysis alongside physics-based analysis.

The data set of conducted pressure and temperature measurement was split into two groups i.e. training and testing. In addition, a K-fold cross-validation was performed on the training set to validate the performance of all considered and selected ML models. The results of each ML model were then compared for accuracy and the Random Forest Classification algorithm was selected. The developed classification model achieved an overall accuracy level of more than 95%. Validating and testing the model on several cases showed promising results as irregularities are detected in advance before engineers evaluate these conducted measurements. The developed model enabled an effective utilization of previous measurements to validate newly conducted ones and, consequently, alert engineers of any detected anomalies in advance. This yielded significant impact on cost and time savings due the model's ability to automatically predict and validate the conducted measurements.

The pressure and temperature validation model enhanced monitoring and interpreting the production/pressure and temperature measurements and resulted in a substantial improvement in timesaving. The model is developed to be run on the Cloud and it provides an automatic validation of the newly conducted measurements. In addition, it also delivers an alerting mechanism to engineers for any observed abnormalities.
Non-Combustion Power Generation Technology for Oil & Gas Production Facilities Towards Net Zero Carbon Emission Agenda
Authors: Muhammad Muhammad Fadhli , Myo Thant Maung Maung , Sedaralit Mohamad Faizal , Shaffee Siti Nur Amira
Published: October 2022

In this paper, a strategy of deploying a novel compact electric generator (n-CEG) is investigated for generation of electrical energy replacing conventional mini turbine generator (MTG) technology. Deployment of these technologies are driven by growing power requirement at offshore facilities with aging conventional equipment currently installed at these facilities. With advancement and potential benefit that the technologies can offer, a high-level feasibility study is needed to ensure suitable technology is deployed. This paper seeks to cover the feasibility of n-CEG technology deployment at offshore facility as compared to MTG and floating solar panel inclusive of technical and cost perspective.
Prediction of Dew Point Pressure for High-Pressure Gas Reservoirs Using Artificial Intelligence Techniques
Authors: Hassan Amjed , Mahmoud Mohamed , Abdulraheem Abdulazeez
Published: October 2022

Dew point pressure is a curial parameter in characterizing gas reservoirs. Several methods can be used to determine the dew point pressure, including laboratory measurements and empirical models. However, laboratory determinations are expensive and time-consuming, especially for studying high-pressure tight reservoirs where more caution and procedures will be required. While empirical correlations do not accurately reflect the complexity of fluid behavior, and limited models were developed for high-pressure reservoirs. The goal of this work is to develop a reliable tool for predicting the dew point pressure for tight and high-pressure gas reservoirs.

This work was carried out using five main phases; data collection, quality control, model construction, development of new correlation, and model validation. The data used in this work were obtained based on 250 laboratory measurements. All data were evaluated and the noises and outliers were removed. Different types of artificial intelligence methods were examined to come up with the best determination model. Artificial neural network (ANN) technique, support vector machine (SVM) approach, and adaptive fuzzy logic (AFL) systems were investigated. The hydrocarbon compositions and the molecular weights were used as inputs to estimate the dew point pressure. Different types of error indices were employed to measure the prediction performance of the developed equation. Average percentage error and correlation coefficient values were determined for the different models.

The developed model predicts the dew point pressure with a percentage error of 4.85% and an R2-value of 0.94. The ANN model developed in this study has 4 neurons and one hidden layer. An empirical equation was proposed based on the best ANN program to provide a direct estimation of the dew point pressure. The extracted equation can provide an average error of 5.74% and an R2-value of 0.93. Overall, the proposed model can reduce the cost and time required for determining the dew point pressure and help to improve reservoir management by providing fast and reliable estimations.
A Drift-Flux Model for Upward Two-Phase High-Velocity Flow in Large Diameter Pipes
Authors: Lee Woochan , Waltrich Paulo
Published: October 2022

This study proposes the evaluation and development of a drift-flux model for upward two-phase high-velocity flow in large diameter pipes. A case where the proposed model is applicable is WCD (Worst-Case-Discharge) calculations for offshore wells. WCD assumes relatively larger pipe diameters and higher flow rates than the flow experiments at laboratory conditions utilized to develop most of the flow models available in the literature.

Most of the two-phase flow models describe flow regime transitions as discrete processes by assigning the required void fraction or velocity for each flow regime transition. Therefore, for each flow regime, flow-regime-dependent correlations or fixed drift-flux parameters are applied, which lead to a wide variety of flow regime maps and complications of flow models.

Unlike others, the model proposed does not have sudden pressure surge or reduction along with flow regime transitions from bubbly to non-bubbly flow regimes. Also, it doesn't have fixed drift-flux coefficients for each flow regime. Further, rather than assigning a fixed void fraction or velocity for bubbly/churn flow transition, it applies the minimum pressure gradient estimated by the bubbly flow model to determine bubbly/non-bubbly flow transition.

The proposed drift-flux model is validated for a wide range of experimental and field data and compared with pressure loss models in commercial software. Among all the models, the proposed model gives the lowest average absolute pressure error (2%) and standard deviation (4.5%) for the tested 279 field data points.
Power Generation Through Back-Pressure Steam Turbine Utilizing Waste Process Steam
Authors: Rege Atul , Dahagam Ravi Chandra , Mukherjee Rahul
Published: October 2022

ADNOC Gas processing fractionation facility have majority of steam consumers at low Pressure (LP) level. Steam is produced at medium pressure (MP) level with superheat to optimize the plant design and piping. Steam generators produce Medium Pressure steam followed by Pressure Letdown Stations (PLS) with de-superheating at various places to cater the demand of Low Pressure (LP) steam. Thermodynamically, use of PLS are viewed as less efficient from energy perspective, while the industry applied efficient alternative is the use of Back Pressure Steam Turbine (BPST) to recover the lost energy in PLS.
How Drone Data Capture and a Visual Intelligence Platform Offer Unparalleled Insight into Capital Project Delivery
Authors: McGinn Barry
Published: October 2022

The construction industry is recognising the increasingly important role of digital technology in delivering capital projects on time and within budget. Over 95% of major projects (over $1bn) are delayed or over budget, according to a report by Accenture. Project owners are under pressure to become more efficient, innovative, and ambitious in how they operate, they are turning to digital technologies to enable full project visibility and evidencing data to make more informed decisions.

The execution of capital projects within the energy sector is typically complex and often relies upon information being shared between the site and globally dispersed project teams. Determining a holistic view of a capital project can be challenging. Storing and interpreting key data associated with planning resources, equipment allocation, utilization and coordinating the delivery of raw materials only compound the challenge of effective reporting.

Traditionally, assessing the status of a project was heavily reliant on paper-based processes. This was a difficult method to collate and manage valuable project information centrally and key stakeholders often had to make decisions based on partial data. The process relied on management's presence frequently onsite and a ‘boots on the ground’ approach to visual inspection. Large volumes of data were often captured by quality inspectors and construction engineers which was an arduous and time-consuming task.
Factory Drilling and Fracturing Technology for Tight Sandy Conglomerate Oil Reservoir in the Junggar Basin
Authors: Chang Long , Zou Lingzhan , Wang Haige , Yu Yangfei , Liu Qi , Zhuo Lubin , Yu Jing , Wu Baocheng , Xu Jiangwen , Shi Jiangang
Published: October 2022

The Triassic Baikouquan Formation reservoir in the Mahu Sag, Junggar Basin is characterized by tight sandy conglomerate oil reservoir with long drilling cycle and great fracturing difficulty. The factory drilling and fracturing techniques of horizontal wells for the Ma131 Well Block of Mahu Oilfield have been determined by regional geological characteristics research and optimization scheme design.

The key technologies for horizontal well drilling speed improvement were explored by designing small three-stage wellbore structure, customizing the PDC bit properly, optimizing drilling tool combination, and selecting the drilling fluid with inhibition and plugging ability. The factory reservoir stimulation technique carried out horizontal well hydraulic fracturing with small well spacing for two sets of beds, and adopted bridge plug + clustering perforation staged fracturing and slick water + gel composite fracturing technology.

12 wells in a drilling pad were arranged in the demonstration area with the depth of 5000m, the distance between adjacent sections was 150m or 100m, with the horizontal section length of 1800m. Batching drilling operation pattern was adopted, with 3⁃well drilling for each of rig. Rapid inclining prevention drilling through vertical sections adopted PDC bit + screw + MWD, with high rotary speed and high flow rate. In the build-up section, radical drilling parameters was used to improve the top drive rotary speed and release the torque. In the horizontal sections, rotary steering tool + low speed high torque screw was adopted. The application of this technology achieved good results, with average drilling cycle of 48.3 d and average ROP of horizontal section increased by 77%. The fracturing construction adopted high pad fluid ratio, high sand ratio and high pump rate. More than 10,000 m3 fluid was injected in a single well with high-intensity proppant injection. The factory operation was carried out in the order of construction from the two sides to the middle well with microseismic monitoring. Factory fracturing with small well spacing generated stress interference and improved the fracture reconstruction effect. Higher shut-in pressure, more microseismic events and large-scale stimulated reservoir volume had been carried out in horizontal wells fractured later.

The factory drilling and fracturing techniques of horizontal wells for the Ma131 Well Block have achieved the goals of improving drilling speed and volume fracturing, which provided technical support for the efficient development of the tight sandy conglomerate oil reservoir of the Mahu Oilfield.
De-Carbonisation Through Energy Management
Authors: Somayajula Harish
Published: October 2022

Borouge, established in 1998 in Abu Dhabi by the Abu Dhabi National Oil Company (ADNOC) and Austria based Borealis, is a leading petrochemical company that provides innovative and differentiated polyolefin solutions. Combining the strengths and experience of its majority shareholders ADNOC and Borealis, Borouge serves a wide range of industries including energy, infrastructure, mobility, advanced packaging, healthcare and agriculture. As a strategic and successful partnership at Borouge, we employ more than 3,100 people with over 50 nationalities, serving customers in over 50 countries across the Middle East, Asia and Africa.

Abu Dhabi Polymers Company Limited (Borouge) ("ADP"), headquartered in Abu Dhabi and the sales and marketing joint venture, Borouge Pte Limited ("PTE"), headquartered in Singapore. ADP consists of the main manufacturing activity of Borouge, whereas PTE consists of the marketing arm of the Borouge business.

Our petrochemicals and polyolefins manufacturing plant is located in Ruwais at a distance of about 250 km west of Abu Dhabi City. The facility is now one of the largest fully integrated single-site polyolefins complex in the world, with an annual capacity to produce 5 million tonnes of polyethylene (PE) and polypropylene (PP). The complex is also the largest Borstar® process technology-based plant in the world, providing enhanced innovative bimodal polymers for a broad range of polymer applications. We remained on track to increase our production through Borouge 4, the next mega-project expansion that will significantly increase our production capacity by 2025. Moreover, we have already started-up our fifth polypropylene plant (PP5) in Ruwais.
Facilitate Geoengineering and Completions with Machine Learning Methods: Case Study from Ordos Tight Oil Field
Authors: Li Jian , Lei Qihong , He You‘an , Han Xueliang , Qi Yin , Mu Lijun , Liu Yuan , Li Haoyan , Ma Fujian
Published: October 2022

Geoengineering is an important integrated approach for oilfield development. Conventional geoengineering focuses on geology and reservoir studies using analytical or numerical methods. A new geoengineering approach uses data mining and machine learning as input to completion optimization. Compared to conventional practice, this innovative approach allows field development optimization to be based on statistics instead of numerical models.

The application of geoengineering to completion means converting geology and reservoir understanding into a form that supports the well completion engineering. Conventional geoengineering work is heavily associated with building geology models and populating reservoir property grids; these require a huge amount of data and human effort to set up and sustain. In addition, in conventional geoengineering for completion, the engineered completion design, production modeling, and pressure matching can show high levels of uncertainty.

The data-driven approach incorporates data mining and machine learning methods to improve the understanding of the reservoir from wireline measurements, geological modeling, and completion practice. Because this approach is based on data and algorithms, the potential exists for improving efficiency and accuracy.

In a case study, data from 130 existing horizontal wells in the Ordos tight oil field were collected for analysis. These data included logging data, well engineering parameters, and production performance data. Beyond data mining, which focused on the data cleanup and visualization, the typical machine learning methods, including analysis of variance, linear discriminating analysis (LDA), and artificial neural network (ANN), were used to improve the logging evaluation, to define the effective sand body and geology quality, to calculate effective lateral length, and to demonstrate trends leading to a completion optimization strategy.

The high efficiency and accuracy of data mining and machine learning contribute to upgrading our understanding of the logging data, reservoir properties, completion engineering, and production performance. With the support from those powerful tools, we can use geological and reservoir input quantitatively for completion engineering, which enables better well planning and better production evaluation.

This case shows examples of data mining and machine learning used in geoengineering for completions. Compared to conventional geoengineering, which is based on analytical and numerical modeling, this innovative approach built on data, statistics, and algorithms has the potential to be more efficient and accurate.
How to Build a Sustainable Offshore Platform: A New Design Philosophy
Authors: Maxwell Stewart
Published: October 2022

It is an unpalatable truth that all engineering and construction works, either onshore or offshore will have an environmental impact, during all the design, fabrication, and operational phases. Balancing the worldwide need for hydrocarbons with the desire to maximise the ESG benefits of an offshore development require a change in the way we approach both platform design and project implementation.

In this paper we highlight the main points in this approach to platform design, and the direct and indirect benefits gained. A case study is presented, where sustainability was at the core of both the clients desires but also in the design philosophy used. The outcome of this project and the benefits gained are also outlined.

The next steps to be considered in this philosophy are also discussed, where additional benefits gained from the use of Digital Twins can be leveraged to provide further potential reductions in the overall carbon footprint.
A System to Inspect Corrosion Under Insulation via Temporal Thermography & Artificial Intelligence
Authors: Al-Jarro Ahmed , Alshehri Ali , Amer Ayman , Althobaiti Abdulrahman , Saiari Hamad , Alsalman Fatima , Albaqshi Muntathir , Chan Godine , Kakpovbia Anthony , Kairiukstis Edvardas , Odisio Matthias , Qian Weiwei , Roy Arjun , Saad Bilal , Shapiro Vladimir
Published: October 2022

In this work, we present a novel Artificial Intelligence (AI) powered non-destructive testing (NDT) system for the detection of potential corrosion under insulation (CUI) inspections, code named DPCUI, developed by the Research and Development Center (R&DC) at Saudi Aramco in partnership with Baker Hughes. This inspection system enables fast external thermographic screening of large facilities by covering many condition monitoring locations (CML), and without any contact with the asset surfaces. It examines temporal thermography datasets that are collected using a high-resolution IR camera, such as those provided by FLIR, and on one or more RGB images that provide context of inspected areas. The collected data is analyzed by a dedicated AI engine to detect the presence of abnormal heat transfer signatures that occur due to defects present within the targeted CMLs. The novelty of this AI powered technology has several advantages. It provides a contact-less, smart, easy, fast, automated, safe, and reliable risk-based repair and maintenance decision making on the integrity of assets, enabling asset owners to efficiently prioritize their operations and processes in a seamless manner while the assets are kept online. Here, we enhance and extend the performance of our AI models to predict not only the presence of potential CUI or not, i.e. binary classification, but also various types of potential CUI, i.e. multi-class classification, a first of its kind.
AI and Digital Transformation: Integrity and Maintenance
Authors: Al Aamri Amran Hamed , Al Fahdi Khalid Khamis
Published: October 2022

Moving forward to Operation & Maintenance excellence in OQ and as per the continuous improvement approach to improve maintenance activities at sites in terms of safety, quality, cost, and time which will reflect on mitigation in risk and redaction in maintenance cost. The quality and the culture of the maintenance team on how to execute the maintenance activities on the field have been improved by the execution of a digitizing project. We moved to digitize field activities MCPs (maintenance procedure craft) as QA and digital collective data (Quantitative/Qualitative) and allowing us to apply QC protocol in maintenance tasks in a proper way. Therefore, the Reliability section started digitalizing maintenance activities via Asset Performance Management (APM) tool to enhance compliance and the Quality Management System of maintenance activities.
Implementation Challenges in Automation of Gas Lift Optimization Workflow: A Case Study for Digital Fields Journey
Authors: Mokhlis Mohamad Mustaqim , Basarudin Mohd Ashraf , Hassan Muhammad Firdaus , Fuenmayor Roberto , Trivedi Rajesh
Published: October 2022

The oil and gas industry has evolved and embarked into the new era, it includes digitalization and cognitive computing, cyber-physical systems, and cloud computing or so-called industry revolution 4.0 or IR 4.0. This industry has been experimenting with artificial intelligence on any task that is time-consuming, requires precise results, and minimizes human error. Most of the giant operators have difficulty managing huge amounts of data that stream from the field every second. Thus, having a good workflow or system that automates the process, reliable data set, and the right people mindset is a huge game changer. These three main components are the main ingredient for digital transformation. Nevertheless, transforming the right people mindset is key for this objective to be obtained and sustained for long run.

Main operator in Malaysia has urge for going digital as part of their transformation journey. By means of collaboration and pace, any operation process or standard operating procedure (SOP) should be reviewed and align with their transformation objective. Nonetheless, managing gigantic data with diverse types and sources from 43 fields is bizarre. This requires a careful plan that consumes lots of resources and effort.

Bringing additional barrels with minimal cost and less effort is the key component of digital field objective. Gas lift optimization workflow is introduced as a workflow that automates the process of optimum gas lift injection rate for maximizing the production using real time data. Gas lifts are being re-distributed across all active producers based on the availability of gas lift supply to identify opportunities to improve production behavior at field level.

Consecutively to accomplish this objective, a special project team is established for initiation, planning, assessment, evaluation, development, deployment, implement and lastly suitability of the digital ecosystem. Through this journey, few challenges were encountered especially when most of the fields are matured fields with lack of real time technologies and some still in analog mode. For marginal field to invest on such technologies will cost them enormous budget and not feasible for the long run.

The objective of this paper will detail out the challenges raised during the project implementation. Some methodologies and strategies of how to overcome the respected challenges will be described further. This paper should be beneficial to all engineers and technologists, especially main operators that plan to take the challenges for digital transformation.