bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reservoir Property Distribution Constrained with 3D Seismic Data in a Tight Carbonate Reservoir: A Machine Learning Based Approach
Authors: Salib Mina Sameh , Alzaabi Naema Obaid , Ahmed Jahan Zeb , Gacem Mohamed Tarik , Abd Rashid Atiqurrahman Bin , Albreiki Warda Salem , Ba Hussain Ali Hasan , Alkarbi Abdulrahman Mabrook , Al Qawadi Hajir Mohamed 
Published: October 2022

Abstract
When dealing with a tight reservoir of basinal settings, depositional facies can show a lot of overlap in their properties. This onshore tight carbonate reservoir, is almost exclusively composed of wackestones to mudstones. The main reservoir quality influencer is the diagenetic overprint reflected in the cementation and dissolution phases, and directly influencing the porosity variation. This made the classical generation of facies trend maps to constrain the properties less reliable, due to poor property discrimination. It demanded the utilization of seismic data to attempt to constrain the properties as much as possible. With the static rock types being sensitive to porosity variation, and porosity having a good correlation to the acoustic impedance, the seismic data may provide key information to obtain lateral trends guiding the properties distribution. A supervised neural network approach was used to provide a link between the lateral variation of reservoir quality and seismic data. The workflow relied on combining deterministic inversion and volume attributes to extract the information. This was followed by multi-attribute analysis to find the best combination of attributes to input into the neural network. Four main challenges had to be tackled. Firstly, an acquisition footprint masking the signal in the seismic data had to be reduced. Secondly, the integration of the seismic inversion output and well information in the modeling workflow. Thirdly, generating and finding the best combination of seismic attributes to fit the given logs. Lastly, adapting the low resolution of the seismic data to the subzones of the reservoir in order to capture the vertical variation. The resulting predicted impedance cube is validated with actual porosity data from wells. The result has proven to be beneficial in building an integrated 3D property model, giving more confidence in the predictability of the properties in horizontal wells and volume estimates. This is seen in the blind tests applied, which show a good alignment between actual and predicted properties.
Innovative Sealant for Mitigating Methane Gas Emission in Abandoned Oil and Gas Wells
Authors: Heras Murcia Daniel , Reda Taha Mahmoud , Stormont John 
Published: October 2022

Abstract
The traditional solution for capping abandoned oil and gas wells is to fill portions of the open well with cement. However, cement is not ideal for plugging or capping because it often results in an ineffective seal. Cement proves ineffective due to shrinkage, inability to bond to steel casing, degradation over time, and a large carbon footprint in production. To be a solution for greenhouse gas (GHG) emissions from abandoned wellbores, plugging must be effective indefinitely. Moreover, the effectiveness must be documented over time. Thus, the conventional approach of plugging with cement and walking away offers no documented proof of GHG emission mitigation.
In this work we suggest the use of a proprietary polymer-based sealant for plugging and abandonment. The sealant, referred to TSN-20, has superior flowability, excellent bond strength to steel and rock, is very ductile, and is thermally stable. TSN-20 sealant can penetrate, flow into and seal extremely thin microcracks (&lt; 30 microns) where cement and most other sealants cannot flow. These small microcracks are problematic leakage pathways for methane and other GHGs. Further, unlike cement, TSN-20 bonds to steel and rock, is very ductile, thermally stable, and resist harsh downhole conditions, including acids and hydrocarbons. Tests showed TSN-20 can dramatically reduce the flow rate after one sealant injection. TSN-20 sealant can be used to mitigate methane gas emissions in abandoned oil and gas wells.
Enabling Shallow Reservoir Development in Malaysian Deepwater Field Via Statically Underbalanced Mud with MPD Applications
Authors: Lee Magdelene Jia Wern , Mohd Sukri Eskandar , Musa Muhammad Idham Adli , Ong Sheau Hun , Bin Abu Bakar Muhammad Aizat , Binti M. Safari Lai Harni Farihah 
Published: October 2022

Abstract
Managed pressure drilling (MPD) technology is a widely accepted technology for wells with narrow pore- pressure/fracture-pressure (PP/FP) gradient window. Whilst MPD is often associated with high capital expenditures (CAPEX) that further erodes the project economics especially for marginal reserves, it does not limit its application since MPD technology can considerably reduce non-productive time (NPT). Most importantly, it is the key enabler to access the previously conventionally un-drillable reservoir target, generating significant revenues to the organization.
As the plan new development well cannot be drilled conventionally by using overbalance mud, numerous dynamic drilling simulations were performed utilizing drilling engineering software to ascertain its drill- ability via constant bottomhole pressure (CBHP) MPD with statically underbalanced mud. As an input, detailed surface equipment specifications, i.e., surface MPD line up and well control equipment are important to ensure successful drilling simulations. Sensitivity analysis using surface back pressure and drilling mud density are performed to determine maximum drillable footage for each hole section. As proper cementing is paramount for well barrier, well drill-ability is further assessed with managed pressure cementing (MPC) simulation to ensure the well is operationally executable.
This paper presents a case history on operations performed in-house well engineering design for a complex and marginal deepwater development field during front end stage with narrow PP/FP gradient window (less than 1ppg drillable mud window) and elaborate on extensive work done for MPD engineering planning procedures in designing a fit-for-purpose well design.
Machine Learning Model for Drilling Equipment Recommender System for Improved Decision Making and Optimum Performance
Authors: Kloucha Chakib K. , El Yossef Bassem S. , Al Hamlawi Imad , M Salim Muzahidin , Pausin Wiliem , Pal Anik , Mustapha Hussein , Shah Soumil , Hussein Ahmad Naim 
Published: October 2022

Abstract
The oil industry, in its constant strive to maximize gains out of operational data is constantly exploring new horizons where to combine the latest advances in data science and digitalization, into the areas where key decisions to drive economical and operational decisions reside with an aim at optimizing the capital expenditure through sound decision making. High volume operational data has been recognized as hiding many opportunities where the captured details these repositories that include real time logs and bit run summaries, provide a clear opportunity where to extract insights to support optimized decisions in terms of equipment selection to achieve the desired operational objectives.
Current possibilities within data science have opened the possibilities through viable solutions, which in this case, aims at providing advise on which equipment in terms of BHA and Bits to select, that would yield the desired outcome for a drilling run. The whole exercise being based on evidence gathered from previous runs where the details for the equipment, the relevant well characteristics, and the observed rates of penetration and the used parameters, are taken into consideration to provide the optimum combination to be implemented in new runs.
The present study describes the methodology in terms of data utilization, data science method development and solution deployment, with the associated issues that had to be addressed in order to provide a viable solution in terms of data utilization, technical validity and final user utilization, as well as a series of recommendations to be addressed within any such endeavors to assure the value addition.
Real Time Data Analysis Unveils Potential Savings in Drilling Operations Through Invisible Lost Time Automated Tool
Authors: Landaeta Rivas Fernando , Cotten Michael , Bimastianto Paulinus , Khambete Shreepad , Krikor Ara , Al Ameri Suhail , Couzigou Erwan , Al Marzouqi Adel , Pausin Wiliem , Al Shahtoor Mohammad , El Masri Hassan , Shabbir Umer 
Published: October 2022

Abstract
Objectives/Scope: Traditionally Daily Drilling Reports were used as the main source of the data input for Invisible Lost Time calculations with great success. One observed drawback was that the hidden causes that lead to invisible lost time were not identified. This paper outlines the framework to incorporate Real-Time high frequency rig-floor sensor data in the invisible lost time calculation, identification, and reduction exercises. This approach breaks down, downhole activities into smaller, measurable, discrete sub-activities that are measured, benchmarked, and targeted for improvement.
Methods, Procedures, Process: This paper covers three major downhole activities: Drilling, Tripping, and Running Casing, breaking them down into thirty-three sub-activities. A three-year drilling activities dataset is categorized into comparable groups based on drilling rig capabilities and formation characteristics. The sub-activities are then presented on a histogram with the average, P10, P50, and P95 values of each sub-activity determined based on a pre-accepted category. This benchmarking process enabled the generation of the targets that are currently being used for calculating and understanding the invisible lost time causes in the operation.
Results, Observations, Conclusions: revious reporting would simply identify the phase of operation that is creating the invisible lost time. Complementing the existing Invisible Lost Time calculation model with Real-Time data has enabled the understanding of the operational steps that lead to sub-optimal performance. A good example of this would be the drilling phase. The new approach is able to pinpoint the steps in the drilling phase that generates the invisible lost time, for example when post-connection times are excessively exceeding historical norms. Another advantage is the ability to run the report in the middle of the activity and highlight that the current rate of execution is not optimal. This enables the supervisor to investigate the situation and propose solutions to improve performance on the ongoing activity. In addition, this approach has been used successfully for ranking the performance of casing running service companies and helping in the decision-making process for awarding new contracts.
Artificial-Intelligence-Based Well Placement Look Ahead Concept
Authors: Mahiout Mohamed Said , Al Enezi Faisal Naif , Santoso Gagok Imam , Ali Elhaj Mohammed Satti 
Published: October 2022

Abstract
Artificial intelligence (AI) brings many benefits to the oil and gas industry and in particular the automation of the geosteering process with structural prediction ahead of the bit to optimize horizontal well placement and reduce the operational challenges. AI techniques are based on data analysis that learn from data, identify patterns and make predictions without the need of direct human intervention. Recent ultra-deep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools have reached a significant depth of investigation capabilities to provide real time information that covers a large volume of the rock around the wellbore. The objective of this paper is to provide details on the automated geosteering methodology used to detect formation changes in real time ahead of the bit based on AI approach.
The new automated geosteering approach is based on a suite of real-time enabled pre-drill and data integration workflows. The latter combines UDAR technology with seismic data. The suite of both workflows as sequential process allows for precise well planning and prediction of a structural model ahead of the bit in real-time. At first, the pre-drill workflow consists of data quality assessment, preconditioning, building 3D tectonic model repository and signal consistent 3D stratigraphic model incorporating offset wells which is constrained by tectonic model. During the second phase with the data integration workflow, ultra-deep azimuthal resistivity inversion output, generating synthetic seismic, extracting from 3D seismic volume along well-path, estimating depth mismatch through non-rigid matching, apply displacement to real seismic and model repository. Therefore, enabling a structural prediction ahead of the bit in 2D and 3D and aiding geosteering decisions in real-time.
The new well placement look ahead concept will enable a right strategy that will enhance the geosteering decisions in real-time. Examples highlighting the value of the AI-based well placement look ahead concept are provided in the paper. Finally, this AI-approach will evaluate the potential added value to the well placement activity.
The proposed workflow for look ahead geosteering based on real time integration of LWD measurements with surface seismic is a major step towards unlocking new horizons of providing critical information hundreds of feet ahead of the bit. This proposed technique will allow the reduction of subsurface uncertainty and helps the achievement of geosteering objectives.
Predicting UCS with Neural Network and Benchmarking Actual ROP for Further Drilling Optimization
Authors: Alexeyenko Alexey 
Published: October 2022

Abstract
Rotary/motor drilling operations amount to approximately 30% of total time of well construction or approximately 800 hrs for gas wells studied in this paper. Reducing drilling time by 10% will result in significant time and cost savings, especially if drilling deep horizontal wells. Time and cost reduction becomes particularly valuable when no extra investments in new tools or materials are required. Optimizing the drilling process using real time feedback from drilling rig sensors is one of the options proved successful by long time practice. Artificial intelligence and its tool: neural networks (NN) is justifiably in the petroleum industry's focus to deliver valuable results with a high return and minimum or no investments. The fact that rock hardness reflects on some drilling parameters is well known. It is natural to try and learn how and to what degree rock hardness can be predicted with use of NN, drilling parameters and well logging records. It is also well understood that the smaller the geographical extent of the formation data sample, the more correlation in properties is observed. This study focuses on wells drilled in the same gas field and cased with the same casing design.
This paper summarizes the study of predicting Unconfined Compressive Strength (UCS) while drilling a horizontal well in highly compacted, cemented sandstone with low permeability and low porosity reservoir (unconventional reservoir) with trained NN in MATLAB environment. Methodology and workflow of arriving at the results of the study is provided in extensive details. The initial goal of UCS prediction with NN is to prove the feasibility of proposed workflow and develop a MATLAB function that predicts UCS, utilizing drilling parameters recorded by mud logging unit and drilling rig's sensors and computing expected rate of penetration (ROP) using common published formula. The final goal of the study is to benchmark actual ROP with forecasted ROP with a high degree of certainty (more than 80% correlation), detect drilling troubles, and optimize drilling parameters for balanced performance and safety operations while reducing drilling time for the given footage.
The study substantiate the proposed methodology and workflow predicting UCS of the formation being drilled and provides input for comparing predicted and measured drilling parameters. The study also verifies and selects which drilling parameters have substantial correlation with UCS. Keeping in mind that it is important to optimize the optimization process. Reducing the number of input parameters for the NN model was also a focus of this study. It was found that surface coordinates, measured depth, rate of penetration, rotation speed, surface torque, surface weight on bit, gamma ray and depth related logged measurements of UCS are sufficient to train the NN model with R2=81%. Data from 9 wells was used in this study.
Maximizing Marginal Fields Profitability Via Drilling Multi-Lateral Wells to Reduce Well Count and Well Cost: Dual Lateral Well with Single ESP and Smart Lower Completion
Authors: Mostafa Mahmoud Mohamed Abdelhay , Kwon Sunil , Oh Hyunjung , Al Braiki Ali Mubarak , Al Shkeili Fatema , El Shahat Ayman 
Published: October 2022

Abstract
Expensive drilling is highly impacting the overall marginal field profitability due to the low reserves and short well life. In this paper, we considered the case of a marginal field with multiple reservoirs stacked above each other with a thin dense layer between each pair of reservoirs.
The initial plan was to drill a single horizontal oil producer well across each of reservoirs. Those wells had planned to equip Electric Submersible Pump (ESP) completions to ensure enough surface pressure to pump the produced fluids to the gathering facility, which is far from the well's surface location, in addition to an expected increase in water production in the early stages of well production. To reduce the overall project cost and to overcome all those challenges, collaborative work resulted in agreement on drilling one well with two lateral horizontal holes across two different reservoirs instead of drilling two single wells, which conflicted with regulations that prevent comingled production through a single completion. To solve regulation of comingled production, a simple smart lower completion with Inflow Control Device-Sliding Sleeve Doors (ICD-SSD's) and swell packers was used to control production from both reservoirs whenever required, in addition to using a single ESP completion to produce from both zones together.
This well design helped to reduce the total drilling cost by USD 5 million while achieving the same production rate that was expected from the two individual wells that were originally planned to produce from the two different reservoirs.
Step Change in Drilling Performance Through Surface Automation in Onshore Abu Dhabi
Authors: Al Hamlawi Imad , Creegan Andrew , Ronquillo Nahum , Baptista Luis , Jalbout Mohamed , Al Nuaimi Mouza , Azizuddin Khaja 
Published: October 2022

Abstract
The process of drilling an oil or gas well is inherently challenging due to the unpredictable nature of many of the variables at play. However, the overall process can be broken down into many smaller phases, each of which has a signature of repeatability. By automating the repeatable processes, and by optimizing the inputs that are a function of variable conditions, we can industrialize the drilling process at a higher level and drive toward consistent, high performance. Improving operational inefficiency requires long-term training or special tools to help address downhole issues. This can be costly and requires many man hours. In the pilot reviewed in this paper, a Surface Automation Solution was implemented to improve well construction performance. The Surface Automation Solution was comprised of a Drilling Automation Platform (DAP), a real-time Intelligent Drilling Optimizer (IDO), an automated stick slip mitigation system, and Automation Lifecycle Management (ALM) supporting services.
The Surface Automation Solution showed extraordinary performance, delivering efficient drilling connections, optimum drilling performance and mitigating drilling dysfunction. New records were achieved in every hole section where the system was operated, resulting in 51% overall ROP improvement compared to offsets, and 44% reduction in stick slip severity, translating to 3.2 rig days savings.
In this paper, we will examine how the Surface Automation Solution saves well delivery time by automating drilling activities, mitigating drilling dysfunctions and optimizing parameters to increase ROP on each section. The outcome is measured by the performance, which in this case is time saved. The data shown is the overall macro key performance indicator (KPI) along with the performance at each individual hole section.
GHG Emissions Tracking on Artificial Lift
Authors: Osman Rami , Bermúdez Fernando , Shono Mohammed , Alnahhas Noor , Yazdani Hafsa , Letan Michael 
Published: October 2022

Abstract
This paper demonstrates a novel approach to tracking greenhouse gas emissions of artificial lift equipment, to support data and data driven decision making in delivering a circular carbon economy. Leveraging the outcome of insights and new datasets through implementing nybl's proprietary novel "Science-Based Artificial Intelligence" to Electrical Submersible Pump (ESP) Lift.ai to wells in the context of Environmental Sustainability and Governance (ESG) in real-world applications. Identifying opportunities to integrate advanced data analytics, Machine Learning (ML) and Artificial intelligence (AI) to improve operational efficiency, production, transparency and reducing downtime, and environmental and social impacts of ESPs, which can also be applied to other rotating, reciprocal and cavitational equipment in the future. Utilizing these techniques in a novel approach will increase transparency and allow active, rather than reactive, ESG efforts and enable data-driven decision-making in this space with limited additional Remote Telemetry Units (RTUs) associated costs - with further opportunities for sites where additional RTU related investments are warranted to drive increased insight.
Transforming Maintenance Delivery Through Scaled Deployment of Connected Worker
Authors: Scott Keith David 
Published: October 2022

Abstract
Paper summarises the learnings from 3 years of deploying Connected Worker solutions across Operations, Maintenance, Integrity disciplines, on both Core and Campaign activities, in Oil & Gas in the UKCS (United Kingdom Continental Shelf). The primary purpose is to share that experience with industry in order to support other Operators in deploying Connected Worker solutions. The paper considers aspects such as the architecture of the system, the evolution of workflows, the integration of the Connected Worker solution into a data lake and the subsequent deployment of dashboards and automated insights to the supporting technical and management teams. Case study examples are included to evidence those points.
Connected Worker technologies are discussed in relation to their potential as foundational requirements for higher order technologies that drive predictive maintenance and the optimization of maintenance, operations and integrity management processes. In conclusion, five features of the architecture of a Connected Worker solution are noted as being critical to delivering a solution that adds value to the business. The five are: (1) Ease of use by field personnel, (2) Ability to integrate easily with existing systems of record, (3) A simple means of generating workflows that is usable by all technical personnel, (4) An efficient and elegant approach to the ongoing maintenance of workflows and tasks, and (5) Ease of integration with other corporate data structures.
Proposed approaches to delivering these requirements that have been previously applied and found workable in service are outlined.