bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Multiple Seismic Attributes Study for Value-Based Insight and Reservoir Characterization of Hydrocarbon Assets - A Data Science Approach
Authors: Roy Samiran , Saikia Kalyan
Published: October 2022

The objective of this paper is to demonstrate how advanced data analytics algorithm with Machine Learning (ML) techniques proves to be advantageous for effective integration of multi-attribute seismic data in reservoir characterization and identification of sweet spots for well location release. The paper exhibits that ML based technique helps revealing sweet-spot insights. The workflow describes how identification of hidden characteristics of data is achieved through this technique. Several ML techniques have found application for reservoir characterization in recent years. This very study based on a use case of an exploratory field shows data driven ML based algorithm can be applied efficiently if the design of the solution is built on a proper understanding of domain and different ML algorithm or techniques. Overall, the ML based workflow can be very useful in early exploration stages of reservoirs where mainly seismic data is available with very few or limited exploratory well data. A novel approach where ML has been used in integration with geo-cellular gridding and multiple seismic attributes; and it resulted a Machine Learning Predicted Composite (MLPC) attribute.
High Resolution Acoustic Analysis for Improved Formation Evaluation of Carbonate and Clastic Reservoirs
Authors: Mahiout Said , Torlov Vladislav , Gouda Mohamed , Abouzaid Ahmed
Published: October 2022

Developing new interpretation methods in line with new technology measurements to improve reservoir characterization becomes a must to overcome the challenges that petrophysicists are facing on a daily basis, among others, thinly bedded reservoirs. The standard acoustic logs vertical resolution (VR) is oftentimes insufficient to resolve the Formation features. In the case of laminated thin beds (LTB), conventional petrophysical characterization has often delivered results, such as hydrocarbon saturation, affected by uncertainties. The main objective of this work is to develop a methodology, and to reliably integrate high-resolution acoustic processing outputs into the Formation evaluation (FE) routines, including the quality controls to identify and remove artifacts and ultimately enhance the accuracy of reservoir characterization.

The approach used in this study is based on the high-resolution acoustic slowness analysis. The measurement standard VR of the acoustic array measurement is determined by the array aperture (the length of the receiver’s array section; generally 3.5 ft), which tends to obscure features that are thinner than the aperture length. The configuration of the highest resolution is 0.5 ft aperture subarray combination, which considers only two receiver levels in the subarray, i.e., single Rx-Rx spacing. The processing has limitations, inherent to physics; one of them is the "Validity condition". The high-resolution acoustic slowness methodology cannot be applied to acoustic waveform arrivals with very long wavelengths. The subarray aperture should be greater than a quarter wavelength of the wave being used. In addition, as increasing the resolution goes hand in hand with decreasing the number of waveforms, the signal-to-noise ratio decreases. There are several quality control and consistency check workflows to be introduced in this study, which will help in differentiating between the real high-resolution acoustic slowness data and any encountered processing artifacts.

These analyses will enable the right evaluation strategy that will enhance the accuracy of FE. Examples highlighting the value of high-resolution acoustic processing/analysis for both carbonate and clastic reservoirs evaluation are provided in the paper. Finally, this approach will evaluate the potential added value to the FE of the heterogeneous reservoirs.

We propose workflows in various Formations and borehole settings to demonstrate that with the aid of enhanced VR acoustic data, the log interpretation becomes more representative of the actual subsurface. The resulting sonic profiles show better consistency with other log responses of similar resolution. These workflows then can be used on a more regular basis, especially in complex situations.
Application of an Integrated Geochemical Approach to Assess Hydrocarbon Presence and its Composition Below the Hitherto Free Water Level. A Case Study from Flank Appraisal Wells of a Producing Field in the Onshore UAE
Authors: Syofyan Syofvas , Ahsan Syed Asif , Koksalan Tamer , Bal Baheeth Asma Hassan Ali , Al-Shamsi Saif Ghanim , El Gazar Ashraf Lotfy , Alkhawaja Ahmed Adnan Ali , Chenji Wei
Published: October 2022

Recent appraisal campaign on a producing field in onshore Abu Dhabi has found evidence of oil presence 60-170 ft below hitherto known Free Water Level (FWL). The Oil water contact (OWC) was identified by electrical and mud logs, geochemistry analyses on core plugs and well cuttings, core stain, core UV, thin sections and Dean Stark saturation. The calculated volume of the hydrocarbon (HC) below the FWL is significant. However, below FWL, one well tested traces of oil. A comprehensive study including geochemistry was commissioned to understand hydrocarbon composition and its mobility for reserves evaluation and future development plan

Total of 1600 ft of core was acquired from three appraisal wells and cuttings from two wells. Pyrolysis was conducted to evaluate hydrocarbon presence and its composition. Subsequently, a subset of sample from pyrolysis data was selected for solvent extraction and detailed analysis for oil characterization. Saturates, Aromatics, Resins and Asphaltene (SARA) fractions were measured on few samples. Finally, Gas Chromatography and Compound Specific Isotope Analysis (CSIA) were performed to compare the extract with the produced oil from the crestal area to assess the variation of HC composition

Parameters derived from mud log have provided a good understanding of hydrocarbon distribution. Thin section and Scan Electron Microscope (SEM) observation showed presence of black, solid carbon particles (Solid bitumen) in the pore spaces despite solvent cleaning. Oil Saturation Index (OSI) and solvent extraction show lower and higher values below and above the OWC, respectively. S1 and S2 yields provided a detailed understanding of hydrocarbon distribution. Saturate composition was highest at reservoir top where the only MDT sample (22% of oil) was successfully collected, whereas it was low where only water on MDT and traces of oil (>99% BSW) were collected during testing.

The pyrolysis data, typically applied to assess source rock quality and maturity, has been utilized successfully in a novel approach to assess the oil distribution and its composition in a conventional reservoir. It is now understood that, in addition to reservoir rock facies, oil facies should also be established to achieve a comprehensive reservoir assessment in this challenging reservoir that has witnessed multiple phases of hydrocarbon charge as indicated by solid bitumen and liquid hydrocarbon. These dispersed bitumen particles are likely to provide large adsorption surface areas for any oil that might otherwise would have been available for flow upon testing.
Gys-Recon a Field Reconnaissance Engine Based on a Combination of Gis, Machine Learning, and Computer Vision Technologies on Satellite Images and Maps
Authors: Elhallaoui Yassine
Published: October 2022

In Field Operations, area information gathering is a powerful step that identifies and locate every Geo-spatial feature present in that area of interest which can provide valuable data that trigger other processes such as operational risks, hazards, and cost analysis and review, one of the usual and common ways to do this information gathering is using vehicles and drive around the area of interest and locate, report what human eye can see to be shared with other involved parties in a report with coordinates, photos, and remarks in overall that gives hints of how the area looks like and the decision will be based on gut feelings and experiences.

The Geographical y-hat system for reconnaissance (gŷs-recon), is a solution not only to identify the presence of features using satellite images and maps in the area of interest but to simulate the real- world operational scenarios whatever is the type of operations using 2D and 3D interfaces, giving statistical figures for every feature such as (Urban areas, Sand dunes, Forests, flat terrain and many more…), that cover the occupation in square meters, the curvature of the terrain, contours and all the necessary Geo-spatial analysis, with the help of unsupervised machine learning algorithms also computer vision, combining those technologies with the GIS analysis, it generates insightful data in number-based forms that can be used in plots, 3D visualizations and creating accurate risks coefficients and percentages.

The system is compatible with most of the GIS input format including the Geo-Tiff that contain the satellite images, the Elevation model data, and the photogrammetry data.

The system results were satisfactory in several ARGAS projects and lead to reassessing some risks and operational calculations.
First 3D Reservoir Model of a Giant Brown-Field (Around 1000 Wells) Built with Zero Pseudo-Wells
Authors: Pamungkas Setiyo , Aidarbayev Sanat , Al-Hammadi Fatema Yousef Ali
Published: October 2022

Middle East brown fields are penetrated by, more or less, around 1000 wells with long-production history. Attempts to incorporate all these wells create huge challenge caused by uncertainty related to well data discrepancy. The discrepancy in acquisition methods, tools, vintages are few factors to name that are root causes resulting in different depth values. Therefore, varying depth makes difficult to build structure of 3D reservoir model without non-geological anomalies such as distorted, collapsed, non-orthogonal cells.

Methodology is to introduce uncertainty to all data feeding the structure modelling process. Following data is used with their ranges of uncertainties: interval velocity, seismic time maps, thickness maps, geological markers and well surveys. These ranges have to be identified quantitatively as they will give us flexibility to integrate data in the model within justifiable windows. Initially, the maps are allowed to change and try to integrate all data without well survey modifications. If, even after number of iterations, data is still not consistent resulting non-geological anomalies, then it is good to try allowing survey to change, but cautiously.

Following application of this workflow, the data started to come in agreement and resulted in smooth, geologically reasonable subsurface structures. Horizontal wells targeting multiple thin carbonates are the most challenging to place them correctly. These wells require a lot of iterations or manual intervention to incorporate in the model, sometimes by adding number of pseudo-wells. Figure 1 shows even those examples can result in geological markers, of both vertical and deviated sections, match structure surfaces where horizontal trajectory at right penetrated layers. Worth to mention that the integration of almost ~1000 wells required zero pseudo-wells that helps to avoid introducing unrealistic noise to the data.

Successful implementation of this project made this giant field one of the first brownfields that incorporated all data in consistent manner without using pseudo-wells. This structure model will maximize the value from ADNOC's existing data resources to reduce uncertainties during subsequent property and dynamic modelling stages plus while drilling future wells. Average estimates show that proper integration of all data can bring minimum $18 million in value.
Thermal Analysis of the Wellbore System During Drilling Operations Using an Improved Drilling Simulator
Authors: Abdelhafiz Mostafa Magdy , Oppelt Joachim , Hegele Luiz Adolfo
Published: October 2022

Prediction of the wellbore fluids and formation temperatures during well construction is an essential step for efficient drilling operation. This work presents the applications of an improved comprehensive drilling simulator for predicting the wellbore system temperature during the drilling process. A fully transient numerical model of the wellbore temperature is developed for drilling and geothermal production applications. The model describes the dynamic behaviour of the thermal state of the wellbore during circulation, drilling and static conditions. The developed model is successfully implemented with the commercial virtual drilling simulator through an application programming interface (API) provided by the simulator. The model is capable to predict the temperature of the wellbore fluids, drill string, casing, cement, and rock formation with variable properties in real-time. This implementation allows the coupling of the thermal model with other physical models, which leads to more advanced and realistic simulations. The model is assessed and validated through a direct comparison with field data. The data is acquired during the drilling of a geothermal well located in the Hanover area in Germany. The results showed a good agreement between the predicted outlet fluid temperature and the measured one. Furthermore, an analysis of the effect of various parameters on the wellbore system temperature is performed. These parameters include drilling parameters such as weight on bit, bit and drill string rpm, circulation rate and drilling rate, in addition to the wellbore geometry parameters such as wellbore radius, casing and drill string sizes. This analysis showed the impact of these parameters on the wellbore temperature profile including the critical areas such as the casing setting point and bottom hole assembly. This information may lead to enhancing the wellbore stability by monitoring the thermal stresses, especially in high-temperature wells. Moreover, predicting the drill bit temperature can result in increasing the lifetime of the bit by adjusting the operating conditions to keep the bit temperature within the specified range. Based on these results, the enhanced drilling simulator with the transient temperature model showed to be a suitable tool for effective well planning.
Methodological Engineering Approach in Designing Injector and Observation Wells Incorporating MMV Requirements in Carbonate CCS Project in Offshore Malaysia
Authors: Balakrisnan Muralithran , Bt A Halim Rahimah , Johan Ahmad Luqman , Ahmad Ani M Qayyum , Ismail Ahmad Fadhli , Yap Yun Thiam , Abd Hakim Hazlan , Abu Bakar M Aizat , Bt M Safari Lai Harni Farihah
Published: October 2022

We will be showcasing the practical approaches / considerations in designing a CO2 injection well covering the engineering aspects, cost optimization and sustainable design for the injection period. This will also include the software aided simulations for the practical load analysis for selection design parameters and materials for the CO2 injector in an optimized manner. We will also address the importance and relevance of the MMV (monitoring, measurement, and verification) method to the CCS project and its influence on the well engineering scope.

To deliver a sustainable well design, we referenced the existing CCS project against those undertaken in other parts of the world and identified the challenges, best practices, and operational approaches undertaken in those other wells. The well design and engineering has taken into consideration the existing internal and industry standards as well as lessons learnt from other CCS campaigns. The evaluation for the well design and construction was conducted to identify potential gaps lacking in the CO2 injection design in offshore carbonate environment. From the identified injection location, evaluation criteria were finalized to assist in decision making for the overall well design requirement covering injectors and observations wells.

The injector well design and construction will be able to successfully counter the subsurface challenges faced in the overpressure clastic formation and depleted carbonate reservoir. The design of the well will also be capable of surviving the external and internal loads due to the injection operation for the duration of the campaign life. Furthermore, the design has taken into consideration the temperature reversal impact on surface and downhole due the supercritical CO2 fluids which has low temperature in nature. We also developed testing procedures and evaluation matrix for the fluid wetted (CO2 injection fluid) sections to optimize material selection for the wetted zone as a major well cost contributor resulting from the CO2 resistance materials. We are converting some of the identified end of field life producer wells into observations wells via sidetracking and workover methods. The current overall well placement between the injectors and observation wells has achieved a comprehensive coverage area for monitoring of the CO2 plume migration across the carbonate field.

This is the first big scale campaign covering the offshore depleted carbonate field as a CCS candidate in the world as previous practices were only undertaken in the clastic environment. Success of this campaign opens more opportunities for achieving net zero carbon emission in the oil and gas industry.
Continuous Dynamic Drill-Off Test Whilst Drilling Using Reinforcement Learning in Autonomous Rotary Drilling System
Authors: Amadi Kingsley Williams , Iyalla Ibiye , Radhakrishna Prabhua , Al Saba Mortadha Torki , Waly Marwa Mustapha
Published: October 2022

In the development of autonomous downhole drilling systems, decision-making in the selection of optimized operating parameters has become one of the technical difficulties. Traditionally, the driller performs a trial-and-error approach in search of optimal parameters, which is now less effective and non-sustainable to the changing drilling environment. This paper presents a decision-making process using reinforcement Q-learning algorithms that can act as real-time optimization algorithms for selecting an optimal operating parameter for rotary drilling systems using Q-learning on experimental published data from the literature.

The reinforcement learning framework is a stochastic approximate dynamic programming, with varying estimation techniques for goal-directed sequential learning from interaction paradigms. First, a Markov Decision Process (MDP) is established by analyzing agent exploration and exploitation of possible actions taken in an environment. Second, the state set and action set are designed by the synthesized consideration of surface operating parameters from the published data within the range of operational limit. Then, sequentially, at each timestep, the agent takes an action (e.g., changing rotary speed or changing axial force) that makes the environment (formation) transition from one state to another. Consequently, the agent receives a reward (e.g., distance drilled) before taking the next action. Furthermore, a recursive reinforcement Q-learning algorithm is developed mainly based on the reward function and update function.

Analysis of experimental data on drilling was implemented for five states of axial force parameters with five feed rate decisions on each of the states, whilst having distance of a hole drilled as a reward. The proposed optimization model computed using value iteration showed that following Decision 2 yielded the best result. The analysis results also revealed that the optimal value function was reached irrespective of the initial state conditions. The agent's objective is to learn policy mapping from states to actions such that the agent's cumulative reward (footage drilled) is maximized.

The result of this research could be used as a decision-making tool in drilling operations that provides an engineered approach for optimal operating parameter selection and improvement in the efficiency of the drilling process in terms of cost and time.
Significant Stuck Pipe Event Reduction Realized Through Structured Holistic Approach Machine Learning and Artificial Intelligence
Authors: Salehi Yasir , Ziada Attia , Rawahi Zahran
Published: October 2022

Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that affect drilling operations. Over several years, Oil and Gas Operators in the Middle East have been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses around 200 days per year in terms of Non-Productive Time(NPT) on stuck events and associated fishing operations.

Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithologies, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of stuck events quite hard to mitigate when relying on human intervention only.

In Petroleum Development Oman (PDO), we lost, between 2013 and 2017, a total of 1,568 days due to stuck events. This equated to a cost of US$15.5 million/year on average, which included the cost of tools lost in hole. In 2018 PDO's Management formed an interdisciplinary taskforce with the objective of analysing stuck events to find solutions which would reduce the cost of stuck events by 50% per year.

At the same time PDO decided to develop an Artificial Intelligence (AI) driven tool that leverages the whole breadth and depth of data (reports, sensor data, well engineering data, lithology data, etc.) available to predict and prevent wellbore stability problems. The tool, known as the "Stuck Pipe & Tight-Spot Event Prediction" (STEP) tool, informs well engineers and rig crews about possible risks, both during the well planning and well execution phase, suggesting possible mitigation measures to avoid getting stuck.

The taskforce's hard and diligent work, along with the use of the STEP tool, resulted in a significant reduction in stuck events, associated time, cost, HSE exposure and production deferment.
Machine Learning Delivers Automated Feedback on Real Time Key Performance Indicators During Drilling Operations
Authors: Krikor Ara , Khambete Shreepad Purushottam , Bimastianto Paulinus Abhyudaya , Cotten Michael Bradley , Toader Lucian , Landaeta Rivas Fernando Jose , Duivala Shahid Yakubbhai , Mughal Muhammad Idrees , Al Ameri Suhail Mohammed , Couzigou Erwan , Al-Marzouqi Adel A/Rahman , Pausin Wiliem , Vallet Laurent François , Chevallier Bertrand , Vaslin Antoine , Ullah Nadeem Hidayat , Qadir Ahsan , Al Khufash Hassan Walid , Shareef Raneef Mohamed , Ul Islam Muhammad Ashar
Published: October 2022

Drilling, tripping and running casing represents approximately fifty percent of the total well time, where the connection time KPI is the common performance indicator for those operations. Therefore, enabling real-time monitoring on drilling weight to weight and tripping connection time KPI's will add significant value through well time saving. The objective of this paper is to discuss the detailed implementation of machine learning to automate the detection and computation of the KPI's in real-time.

The existing method for drilling performance monitoring requires extensive human data interpretation to calibrate the parameters required in this process. To overcome the complexity and reduce the human interaction, the automated Rig state and Drill state activity level were implemented based on Machine Learning (ML). The algorithm learns from the previous connections, drilling stand or tripping conditions to define the thresholds necessary to determine the current rig operation. With automatic rig activity detection, statistics to monitor the performance can be done in a systematic way. As a result, consistency of computation allows to compare performance and to improve it.

The automated process using Machine Learning (ML) delivered consistent and powerful real time KPI computation, this helped to eliminate any human interpretation. This enabled real-time performance analysis delivery to rig site operations team. The machine learning model results were compared with the existing performance engine output and the comparison showed accurate and identical rig state/drill state detection and KPI's computation.

The initial potential time saving with the implementation of this methodology is estimated around 15%, this was achieved through performance improvement on drilling and tripping connection KPI's. Further potential time saving can be achieved by extending the concept to track casing and liner running performance monitoring and other relevant drilling activities.

This project introduces novel Rig state detection and KPI computation based on automated machine leaning model, demonstrating the benefits through improvement in drilling performance. The approach allows operators to mitigate data issues related with human interpretation and demonstrate real-time, high frequency and high-accuracy KPI's to significantly improve the drilling performance.
Drilling and Cementing Through Highly Active Shallow Gas Field
Authors: Alfakih Abdulqawi , Galaby Amir , Sadiq Nauman , Famiev Robert , Nafi Mohammed Khalil
Published: October 2022

Drilling and cementing through surface sections with highly active shallow gas formations is seriously challenging. In order to save employees lives and maintain a safe working environment, wells are designed with careful consideration of mud weight, drilling and mud parameters, while utilizing advanced monitoring techniques during well execution. While drilling, if well flow situation occurs, killing the well using industry accepted killing methods is not always possible as proper blowout prevention systems are not yet installed at the wellhead. For such wells, a diverter system must be utilized, enabling closing the well and diverting the flow to a safe distance away via two large sized vent lines for the rig crew to evacuate the work area safely. In cases where shallow gas contains flammable hydrocarbons, evacuation is mandatory. In some places in the Middle East however, the shallow gas has different properties, being inert nonflammable Nitrogen (N2) gas, eliminating both explosion and fire risks. Nevertheless, handling high pressure blowouts introduce severe risks to crew members, equipment, and the environment. In case drilling is decided to be initiated in the high-risk shallow gas area, all precautions must be taken to prevent gas flow whilst drilling. This is possible by adequate mud weight selection allowing sufficient overbalance throughout the complete drilling, casing, and cementing operations ensuring shallow gas zone is properly sealed and isolated before drilling deeper intervals.

In a large Middle East field, a large number of wells have been drilled with some areas having shallow nitrogen (N2) gas at high pressure in formations lying at 200-800 ft depth. In such areas, having shallow N2 gas flow is inevitable. Due to the limitation of surface locations, and in order to develop all field recoverable reserves, the only way is to drill wells in the highly active shallow N2 gas areas. The field is developed by drilling costly horizontal multilateral wells with maximum reservoir exposure. Wells typically start by drilling two large sized surface sections, a 28-in which extends to +/-160 followed by a 22-in wellbore sections. While drilling the 22-in section only extends to +/-1200-1800 ft, however, multiple drilling challenges have been observed as a result of the presence of a highly active shallow N2 gas formation. Gas influx while drilling, well flowing and gas channeling after cementing, and continuous gas bubbling in the cellar represent the main challenges experienced in the 22-in surface section.

This paper will present details of the engineering analysis performed and the procedure developed to enhance well design and cementing through the highly active shallow N2 gas surface section.
Safer and Faster Drilling through AI Driven Permanent Cuttings Monitoring - An Operator's Approach
Authors: Ripperger Georg , Peisker Jörg , Oberschmidleitner Patrick , Kucs Richard Josef Werner , Winkler Doris
Published: October 2022

Having the ability to study the solid cuttings separated on the shale shakers can provide valuable insights into hole cleaning, state of the drill bit, mud and shaker performance and potential damages to downhole equipment. Although studying the lithology of the cuttings can serve as first visual confirmation of formation changes, cuttings volumes allow judgement of hole cleaning and assess whether wiper trips are needed or can be avoided. Cavings on the other hand, can be a first indicator for problematic wellbore stability conditions and can be used to stay out of costly kick situations. Despite this potential of vital information, shakers are rarely equipped with sensors. The equipment is monitored manually by field personnel and material samples are taken only discontinuously. This in return poses a high risk in receiving information too late to react to or miss it altogether.

The operator believes that closing this gap is a crucial factor for drilling automation and optimization and that this can be done best by real time video analysis. Apart from cuttings volume flow and cavings detection, the operator, deemed an additional benefit in demonstrating in-house capabilities in the field of machine learning (ML). The ease of internal data accessibility in combination with a small overhead was key for a fast and thus cost-efficient development.

The project presented in this manuscript, was executed using multiple machine learning models trained in-house that proved the concept of providing average cuttings size and sieve coverage in real-time from a video source. Subsequently, the operator built a test stand and conducted a series of static and dynamic experiments using regular, LIDAR and stereoscopic cameras. The learnings from the laboratory work were applied in an additional test series on an onshore rig to validate the laboratory results under field conditions. Ultimately, the scalability of the proprietary ML model was tested with video samples from already installed equipment on drilling rigs.

The project successfully demonstrated the feasibility of using deep learning and machine learning approaches to introduce camera-based solids monitoring to the drilling industry. Despite the fast development time, it was possible to successfully recognize cuttings, cavings and anomalies in the solids output using proprietary machine learning models and regular off-the-shelf hardware. The acquired data will be used to calibrate simulations and monitor deviations between modelled and actual data in real-time. Based on the excellent technical feedback, economic value of the underlying business cases and fast-track in-house development, pilot projects are in the pipeline to lift these new tools to the next technological readiness level.
Improving Drilling Performance Using Drilling Efficiency Optimization (DEO)
Authors: Al Braiki Abdullah , Taha Ahmad , Al Rubaiey Hilal , Al Naabi Hashil , Al Lawati Yasin , Al Subhi Anees , Al Hinai Qasim , Radwan Mohammed
Published: October 2022

Petroleum Development Oman (PDO) is the leading Exploration and Production company in The Sultanate of Oman, and it delivers the majority of the country's crude oil and natural gas supply. It also has a great focus on delivering excellence, growth, and sustainable value creation within and beyond the Oil and Gas Industry. It is a pioneer in adopting state of the art technologies to run its business and to even goes beyond the Oil and Gas industry by supporting the nation in capability building and sustainable development. This is but an implementation of Petroleum Development Oman's Vision which is to be renowned and respected for the excellence of our people and the value created for the Sultanate of Oman and all the stake holders.

With the heavy focus from the world in the recent years on Digitalization and Transformation, Petroleum Development Oman has set its sails in the Digitalization journey by creating a dedicated team that supports the company's business through digitalization. With the focus on Digitalization comes the utilization of Artificial Intelligence. The driver behind this focus is to have an increased efficiency, achieve greater business agility and creating value for the business in an environment where the oil price is highly volatile.

In the Oil and Gas industry, drilling performance is key to reduce the overall cost of the well construction. Different companies strive for performance improvements through many ways that include technology, optimization and recently, digitalization. Automating the Drilling Efficiency Optimization (DEO) is one example of applying digitalization in the Oil and Gas Industry. DEO is a tool that was developed over the years to suggest the optimum parameters that lead to optimum performance based on historical data. In the past, the DEO Roadmap construction was done manually. It was time consuming and had limits when it comes to amendments as the data can be updated only after drilling a well. Nowadays, Petroleum Development Oman has managed to automate the construction of the DEO Roadmap and allow it to be updated continuously in a feedback loop using data mining and Artificial Intelligence.DEO is now set to process over 500 wells every year with a target to improve and optimize drilling efficiency.This has led to improvements in people's efficiency as it takes less time to view the roadmap. DEO was piloted in of the deep wells where hard rock is drilled and It led to improvements in the drilling performance where savings as high as $430,000 per well were achieved.
Single Trip Multizonal Resin Sand Consolidation and Fracturing, A Novel Technique in Sand and Fines Control
Authors: Rozlan M Rizwan , Salleh Nurfarah Izwana , Magna Bela Sunanda , Misron M Al Perdaus , Kumar Suman
Published: October 2022

Objectives/Scope

An Unconsolidated Near Wellbore (NWB) area of the formation as well as the high fines content in the formation have led to massive sand and fines production and therefore, Loss of Pressure Containment (LOPC) at surface facilities. Resin sand consolidation to strengthen the NWB area and fracturing to immobilize fines during production are necessary. However, performing both resin sand consolidation and fracturing completions in multizonal reservoirs is too costly. Therefore, a single trip multizonal sand consolidation and fracturing job is performed to reduce operational days and cost

Methods, Procedures, Process

Three (3) different techniques to perform resin sand consolidation and fracturing are compared. First, is using a "Mechanical Service Packer (MSP) with Sand Plug" to perform both resin sand consolidation and screenless fracpack jobs. Second, is using "MSP with Sand Plug" to perform resin sand consolidation and to utilize Multizone Single Trip (MZST) tool to fracpack. Third, is using a Straddle Packer to perform resin sand consolidation and to utilize MZST tool to fracpack. The first technique was performed in wells W-1 and D-1 and the third technique was performed in well H-1

Results, Observations, Conclusions

The technique to perform resin sand consolidation as part of the primary sand control method in a new well followed by fracturing the formation to immobilize the fines is a novel technique. The utilization of a Straddle Packer to perform multizonal single trip resin sand consolidation followed by running MZST tool for fracturing has managed to reduce the operational days by more than half compared to the other techniques. Sand and fines production has been reduced significantly in wells W-1 and H-1 as a result of the novel technique.

Novel/Additive Information

The decision to perform resin sand consolidation prior to fracturing the reservoir is dependent on the Ultimate Compressive Strength (UCS) of the rock. Unconsolidated formation with UCS less than 2,000 psi is to be strengthened via resin consolidation. The single trip multizonal resin sand consolidation via straddle packer was first performed in well H-1 with 6 zones.
A Novel Engineered Drilling Solution for Improving Backreaming and Drilling Efficiency
Authors: Wong Chee Wah , Sluys Nicolas , Khirfan Mohd , Unnikrishnan Nitheesh Kumar , Al Ameri Saleh , Al Yamani Sultan Mohamed
Published: October 2022

The wells were drilled in the Al Nouf field which is part of The Northeast Bab (NEB) fields comprise three major structures containing some of ADNOC Onshore's largest oil reserves namely Al Nouf, Rumaitha and Shanayel, covering approximately 1,200 square kilometers and located approximately 60 km west of Abu Dhabi city. The main hydrocarbon reservoirs in these structures are Kharaib-2 (Thamama Zones ‘B’) and Kharaib-1 (Thamama Zones ‘C’). The Al Nouf field lies on a coastal/shallow marine area and presents special environmental concerns due to the proximity of delicate coastal ecologies, while the Rumaitha and Shanayel fields are located entirely onshore, in a desert region some 30 km onshore from the coastline.

The wells drilled in Al Nouf fields are cluster ERD wells with 10m spacing which are deviated from the surface. The drilling challenges include surface collision risks, unconsolidated surface formations in the surface section and total loss zones, and highly reactive shales in the intermediate section. Extensive back reaming while pulling the string out of hole may be required, with challenges often localized across problematic formations but also spreading across the entire open hole section.

Mud rheology modifications have not proved effective in mitigating back-reaming events, so a new approach has been investigated consisting of the integration of a reaming stabilizer within the bottom hole assembly (BHA). The customized reaming stabilizer is designed with passive Polycrystalline Diamond Compact (PDC) cutters mounted on the leading and trailing edges of the blades, while Tungsten Carbide Inserts (TCI) are fitted along the gauge. The tool replaced the upper-most conventional stabilizer to provide a much more efficient reaming ability while limiting any impact on the directional behavior and bending stress state of the BHA.

The paper will discuss how the substitution of conventional stabilizers within the drilling BHA with dedicated reaming stabilizers resulted in significant improvement in the wellbore condition, allowing drilling and back-reaming operations to be more efficient, and has ultimately helped drive the well construction cost down.
Safeguarding the Most Valuable Asset in Critical Sour Facilities
Authors: Maskari Mubarak , Habsy Zayid , Murugiah Jawahar Kumar , Tallentire Lesley
Published: October 2022

The relevance is on South of Oman operating critical sour fields with high pressure (500- 1000bar), H2S, CO2 up to 10% and 25% respectively. The field operations team, maintenance personnel, contractors, vendors conduct their activities with proper PPE adhering to safe operating procedures. However, during emergency, there is always a challenge to track, reach out and respond to people within Emergency Planning Zone (EPZ) extending to 100's of Sq.Km. This paper highlights the challenges in Emergency Response process - release of H2S gas cloud and the solution by embracing the digital technologies during these "mission critical" operations

A project had been taken on board to address the above-mentioned Emergency response challenges for real time tracking of all workforces including contractors within EPZ area and respond in a timely manner. Four key critical sour operating facilities with different complexities, infrastructure, and aging order had been considered. Company together with solution supplier had selected a cloud connected personal gas monitoring device with real time location tracking, voice over capability and multi gas sensors after thorough evaluation of sensor compatibility, Compliance to standards, Data security, Total Cost of Ownership and Deployment Staircase. Implementation had been mapped adhering the baseline stipulated in corporate Emergency Response Procedure including the mitigation controls

Different valid observations are recorded from the team travelling to sour wells / Manifolds scattered at remote locations with distance ranging from 20 km to 80 km from the central processing Facility and field operating personnel working in High pressure, sour Gas Injection compressors with discharge pressure up to 450barg. The application has been scaled to cover the red zones as well yellow zones for tracking people in Central processing Facility. Well defined feedback strategy based on Bottom-Up approach, sessions with super users, Dynamic scenarios as part of routine emergency drill with the front-line team together with key stakeholders had resulted in enhancements along the product roll out. Flexibility in accessing the application from control room, Local Emergency Control Centre through office domain

Network and the data visualization were proven to compliment the present emergency response capabilities. Ultimately, the team could react, respond, and reach out to people during H2S gas release scenario in a timely manner The team has adopted Industry 4.0 digital transformation - cloud hosted Software as a Service application with real time data available at endpoints, accessible through Role bases access control privileges. The Post Implementation Review results on sensors, application, effect on work process and culture will be part of the detailed presentation
Competitive Commercial and Technical Advantages of Digitization for On-Demand Additive Manufacturing in Oil and Gas Industry
Authors: Awadallah Mohammed , Abdelkader Mustafa , Younis Farah , Bin Zubaa Yaser , Al Zaabi Fatima , Al Ali Ali , AL Aidarous Adel , Carreiras Pedro
Published: October 2022

Adapting new technologies in digital transformation within ADNOC is in-line with its 2030 strategy for a smart growth, resulting in maximizing the value of every barrel. 3D printing enables ADNOC to be self-sufficient in sourcing spares locally (especially Business-Continuity). Hence, boosting UAE's economic performance through national investment and leadership.

Resilient supply chain management is the main contributor towards the success of future organizations. This enables these empower these organizations in terms of performance, profitability, efficiency, and sustainability that will directly ensures business continuity. While effective supply chain is directly dependent on logistics, inventory control, and materials handling, it is also strongly interlinked with the utilization of new technologies that will enhance the complete supply chain cycle.

This paper demonstrates a hands-on achievements in terms of introducing three-dimensional (3D) printing to ADNOC Group Companies, taking into consideration the challenges that this technology is facing to penetrate the conservative oil and gas industry and realizing its potential benefits to the business. Additionally, the importance and the achievements towards constructing Digital Library in ADNOC specifically and oil and gas companies generally was discussed along with feasibility study which was carried out to evaluate the process from the environmental and techno-commercial perspectives. The technical results exhibited advantage in matching plant's requirement in terms of performance and quality that matches or even exceeds the OEM spares. Whereas commercial advantages with around 50% savings in direct purchases and lead-times, and finally 90% reduction in carbon footprint. Furthermore, a forecasted consumption and saving within all ADNOC Group Companies was extrapolated which shows AED2.1billion worth of products will be made in UAE by 2030 once this technology adopted down the line.
Soft Computing Approach for the Prediction of Surfactant Adsorption
Authors: Kalam Shams , Abu-Khamsin Sidqi A. , Kamal Muhammad Shahzad , Patil Shirish , Al Shalabi Emad W.
Published: October 2022

Surfactants are commonly used in chemical enhanced oil recovery (cEOR). The quantity of surfactant loss due to adsorption on a rock directly influences a cEOR project economics. Therefore, surfactant adsorption quantification is an important area of interest. Surfactant adsorption is greatly influenced by the mineral composition present in the rock. This paper presents a novel machine learning (ML) intelligent model to predict surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. Several pure minerals were used to determine the static adsorption of a novel cationic Gemini surfactant. The novel surfactant is compatible with high salinity and high-temperature environment. XRD was utilized to show the percentage of the rock-forming minerals. The solid-liquid ratio used in this study was 1 gm in 15 ml, and the time given for rock fluid interaction was 24 hours. The supernatants obtained after 24 hours of shaking and 20 minutes of centrifuging were analyzed using high-performance liquid chromatography to determine the remaining surfactant concentration. ML algorithms were applied to the dataset to predict surfactant adsorption. Hyperparameters tuning was performed using K-fold cross-validation integrated with an exhaustive grid search technique.

Surfactant adsorption isotherms were constructed from the real experimental data for each pure mineral. The dataset was divided into an 80:20 ratio for training and testing, respectively. Extreme Gradient Boosting (XGBoost) and random forest (RF) techniques were applied to the training dataset to predict the surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. The remaining 20% of the dataset was used to test the models. The evaluation error metrics comprising R2 and RMSE showed good agreement of predictions with the unseen data. Also, it was found that XGBoost outperformed RF in surfactant adsorption predictions with R2 of 0.9914 and 0.8990, respectively. The developed model can be used to predict surfactant adsorption by using mineral composition and surfactant concentration. The developed model saves a significant amount of time in running the tedious and time-consuming experiments and helps to provide a good quick estimate of surfactant adsorption. This model will add a great value in the practical application of a chemical EOR project.
3D Printing Laterals for Sand Filters
Authors: Banna Rakan Wajdi , Orfali Ahmed Mohsen , Elayoubi Ensan Mahmoud , Asiri Abdulaziz Yehya , Baig Mirza Mansoor , Khan Imran A.
Published: October 2022

The idea is to use 3D printing technology to design and manufacture wedge wire laterals of sand filters with non-metallic material to withstand the filter operating conditions of treated seawater for oil and gas fields and reservoirs. The design of the laterals is unique and can't be achieved with conventional nonmetallic manufacturing methods due to wedge wire laterals design specifications. This modification will resolve major corrosion challenges, sand migration and meeting water quality specifications in addition to enormous cost savings compare to existing metallic materials.

Wedge wire laterals WWL of filters are designed to remove particulates from fluids to prevent contamination, media passing or damage to downstream industrial processes, equipment and oil and gas reservoirs. WWL is the main element for the filtration process and any failure or leaks will lead to media passing and affect to all downstream piping, equipment's and meeting water quality specification.

The existing materials is stainless steel 316 and it's has frequent failures, corrosion and pitting even before the T&I due date, as shown in below picture. The design of the laterals is unique and can't be achieved with traditional manufacturing methods of nonmetallic materials due to laterals technical specifications.