bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Future of Acid Corrosion Inhibitor Testing: A Machine Learning Solution
Authors: Ureiga Sulaiman , Hajri Nasser , Green Todd , Javed Muhammad Imran 
Published: February 2024

Abstract
The paper aims to present a machine learning virtual laboratory as an alternative to autoclave testing, which is typically utilized in determining corrosion inhibitor performance. The virtual lab aids in the optimization of acid corrosion inhibitor loading for stimulation jobs. The lowest acceptable/safe chemical dosage of the inhibitor is the optimal acid corrosion inhibitor loading. Hundreds of physical autoclave test results from a variety of settings were used to develop and test the virtual laboratory's underlying machine learning models. Metal coupons are tested with acid recipes in a laboratory autoclave under simulated wellbore conditions. The weight loss per unit area is measured in the autoclave lab test. The weight loss per unit area is measured in the autoclave lab test. Expected acid exposure period, acid strength or concentration percentage, downhole temperature, corrosion inhibitor type, corrosion inhibitor loading, and steel type are among the parameters that affects the testing. The results of the laboratory tests are categorized into two groups. One set includes 80% of the total autoclave tests which is used to train the machine learning model. The remaining 20% is used in a completely different set designated as the testing set. The testing set is used to validate that machine learning model predictions are accurate. For training and testing, several machine learning models are applied to the same data sets. Field engineers can utilize the machine learning model with the best confidence to design acid stimulation recipes with acceptable corrosion metal loss and low cost. The model can be dynamic, with updated tests being uploaded to the model on a regular basis to train it and improve prediction confidence. Eight machine learning models were trained and then tested through their tests. Each machine learning model tested the sensitivity of testing parameters using four distinct input cases to determine the best machine learning model. The outputs of two machine learning models have a 97.90 percent confidence level. The virtual laboratory will assist oilfield operators in lowering the costs of acidizing treatments while maintaining safe corrosion inhibition limits. In addition, the virtual lab would reduce the requirement for laboratory tests to determine acid formulas for each field job.
Petrophysical Properties Derived from High Frequency Drilling Vibration for Geosteering- A Feasibility Study
Authors: Al-Ofi S. , Ma S. , Ali S. , Maity S. 
Published: February 2024

AbstractDrilling vibration is usually used for drilling operation diagnostic for evaluating downhole fatigue failures, severe wear of bottom hole assembly (BHA) and bit, and wellbore instabilities. This study highlights how formation petrophysical properties such as porosity can be estimated from certain range of high frequency triaxial vibration data, so that quality of geosteering may be enhanced in challenge conditions, such as ultraslim hole drilling where limited logging while drilling (LWD) data may be acquired.In this study we explored some data sets of triaxial accelerometer measurement module, in addition to density, neutron, resistivity, and gamma ray. Although typical accelerometer measurement is sampled every 5 seconds, we customized the accelerometer recording to capture axial, lateral and tangential vibrations at 2,500 Hz sampling rates. These high frequency vibration modes were processed in frequency domain by use of fast Fourier transform and analyzed, in terms of frequency and amplitude, against other drilling parameters such as weight and torque on bit. Data regression and correlation were investigated against formation properties derived on the same drilled section of LWD logs.Example results for a tight carbonate formation showed qualitative correlation between axial vibration average amplitude values and LWD neutron porosity. In addition, when we analyzed the high frequency vibration data and decomposed it to different frequencies, we observed higher frequency vibration peaks, 500Hz and higher, are highly correlated to LWD formation neutron porosity and bulk density. Axial and tangential vibration modes showed better correlation with formation properties than lateral mode. Taking weight-on-bit, bit size, drilling torque and rotation speed into consideration, one can use inverse correlation regression to obtain reasonable porosity values from high frequency axial vibration peak values.These findings open the path for geosteering, and maybe even for formation evaluation, capabilities in complex drilling conditions where BHA is restricted such as in ultraslim hole under balanced coil tubing drilling.
Automation and Standardization to Accelerate Well Delivery and Resource Allocation, in a Complex Field Development
Authors: Al Abry M. , Al Mahrouqi B. , Al Farsi S. , Al Busaidi S. , Al Hosni Y. , Al Shabibi H. 
Published: February 2024

Abstract
The development of oil fields are becoming more complex and more challenging as the industry moving away from easy oil, the pursuit of efficiency, reliability, and consistency has become paramount. One strategy that organizations often employ to achieve these goals, especially in complex oil fields development, is automation and standardization of repetitive process.
Automation refers to the utilization of technology and software systems to perform tasks and processes with minimal human intervention, improving work quality and Standardization, on the other hand, is the process of establishing uniform templates and guidelines, and specifications across the organization. It aims to create a cohesive framework that ensures consistency in product quality, service delivery, and compliance with KPI's.
The convergence of automation and standardization in a complex field represents a transformative project that can redefine how organizations operate. Such projects involve the integration of automated features and the establishment of standardized practices to not only enhance efficiency but also improve quality during planning phase but as well as allowing time to dive deep, react and enhance the end results.
Well delivery planning and proposals are fundamental steps in the oil and gas industry, serving as a roadmap for drilling pro jects and providing stakeholders with the information they need to make informed decisions regarding investments and resource allocation. These processes require thorough analysis, attention to detail, and adherence to industry best practices and regulations. Fifteen well planning proposals were selected to implement those automated workflows via existing tool and software with standardized outputs which are relevant to production geology input. The targeted reservoir is a Fluvial system with channelized stratigraphic trap, stacked heterogenous reservoirs. The complexity of the field was captured with range of Models and scenarios. Which later used for the planning the field development. The well selection process had to reflect for the high range of uncertainty and complexity of the reservoir. The implementation trail was led to a time saving by 45%, less iterations between contributes and peer assist and improved work quality. this project was implemented during the business cycle of resource allocation in which the benefits and the values were remarkable.
The initiative of automation and standardization will be expanded in future to cover all business cycles in field development, modelling, well planning, end of well report, reservoir management activities and reserve classification and reporting.
Early Warning Method to Detect Mud Motor Stallings Incidents in Drilling Operations Using Machine Learning
Authors: Zomarah B. , Alotaibi B. , Zahrani M. , Alsaihati A. 
Published: February 2024

Abstract
In recent years and with the increase of complex operations such as directional drilling, there has been an increase use of mud motors to drive performance and deliver more complex well design. The challenge is that motor failures increased due to the complexity of the operation which tends to have a significant impact on the well delivery and cost. Like any equipment failure, it is impacted by the conditions the tool has been through and it often provides prior warning signs that if missed will have significant non-productive time. One of the major motor failure signs is the motor uncontrolled stalling. The objective of this paper is to present an early warning method to detect and alert of mud motor stalling to the rig crew to react before it leads to other undesired conditions. Motor stall occurs when the mud motor stops working which will cause stalls with a high-pressure reading. Using daily drilling reports (DDRs) and real-time data surface parameters, subsurface data, a dataset of incidents was created by SMEs and it is fed into a machine learning model to detect early warning signs of stalling and provide the rig crew with detailed reports in real-time. After applying the proposed data-driven method, the stalling events can be accurately detected with +80% accuracy and a low false positive rate. To ensure proper mud motor monitoring; DDR, BHA, and relevant data such as motor specifications are considered. The result of such work is impacted by the data quality of real-time data such as uncalibrated channels, improper units, and significant noise in the data. This work extends the existing literature by using surface and subsurface data to accurately detect early warning signs of motor stalling. Our work presents a complete framework for how stalling events can be detected and reported in real-time.
Elastic FWI of Surface Waves for Near-Surface Characterization of Middle East Land Data
Authors: Sedova Anna , Reinier Mathieu , Donno Daniela , Lambaré Gilles , Carotti Diego 
Published: February 2024

AbstractThe south of the Sultanate of Oman is characterized by very strong lateral and vertical velocity contrasts that make seismic imaging particularly challenging. In this context, full waveform inversion (FWI) appears promising. The first attempts based on acoustic FWI implementations faced difficulties in this region because the characterization of the complex near surface is critical for the success of acoustic FWI and imaging of the deeper layers. Acoustic FWI was initially coupled with near-surface characterization methods, such as inversion of the dispersion curves of surface waves, which introduces some elastic properties in the process. We take here a step further by applying elastic FWI to surface waves for an improved near-surface characterization. Since surface waves are primarily sensitive to Vs, the associated inverse problem is less prone to cross-talk with other elastic parameters. Moreover, our workflow benefits from the ultra-low frequencies recovered by interferometry. We present an application using a dataset from the south of the Sultanate of Oman that combines elastic FWI for Vs using virtual and active data and utilizes this Vs model to constrain the shallow Vp model.
A Newly Developed Drilling Rate Model Optimizes Drilling Efficiency Using Artificial Intelligence
Authors: Al-Rubaii Mohammed M 
Published: February 2024

Abstract
Rate of penetration is defined as the depth of the drilled rock per time unit while drilling. Alternatively, drilling rate or rate of penetration (ROP) is the speed of drilling. It is usually stated as drilled footage per hour (ft/hr). ROP has a major impact on the cost optimization and accelerating delivery of wells. ROP is a critical factor in calculating cost per foot in each well. ROP optimization ensures efficient mud solid equipment by controlling the mud control system and its compatibility with drilling cuttings. It is also valuable to optimize the ROP without jeopardizing the well drilling performance order to avoid the inducing drilling problems.
The objective of the introduced paper is to present a new rate of penetration model that combine the mechanical parameters, bit and rig hydraulics, and drilling fluid rheological properties. The model has been validated in the field, and compared with offset wells. The results has shown improvement of well drilling performance and safe optimization. Many of ROP models were developed before such as ROP model based on drilling fluid parameters, surface drilling parameters, or machine learning models or on bit design modifications models. Noticeably, those models have not been validated, and not feasible for drilling operation.
The new ROP model combines several factors, has been validated, and designed for drilling operation. Mechanical parameters, rheological properties, bit & rig hydraulics were collected for the same field, hole section, and mud type to ensure the compatibility of the developed model with applications of ROP optimization for the given field. The relationships between ROP with mechanical parameters, drilling fluid rheological properties and bit and rig hydraulics were analyzed and evaluated to conclude the best and strongest relationships. The optimization was used and implemented by including valuable and novel technique that enabled the developed model to perform significant improvement in comparison with drilled offset wells. The result showed the validation and comparison values of measured ROP was 95 % of regression which is a high value of squared regression of R2. By comparing the optimized ROP in real time with offset wells’ ROP, 50% ROP improvement was recorded. The developed model can tell the user the optimized drilling parameters, proper drilling fluid properties, enhanced rig & bit hydraulics that can lead for comparable and proper optimization without jeopardizing the well drilling performance. The developed model can be easily link to real time operations center (RTOC) to ensure real time monitoring model and ROP analyzer system. The new model can be compiled with other artificial intelligent tools in real time.
Digital Well Construction Environment Enables Holistic Risk Management and Optimization in Mature Field
Authors: Bone Kim , Castro Boris , Hummes Olof , Karimi Hamid , Marwah Gaurav 
Published: February 2024

Abstract
In a mature, conventional hydrocarbon field, increasing variance in well designs is required to exploit remaining reserves. Unplanned well events cause non-productive time (NPT) in drilling operations, where root cause must be understood to tailor and implement solutions. The interpretation of offset well subsurface data is considered key in overcoming drilling challenges and for the cost-effective planning and delivery of further development wells in this environment. An integrated well construction software with holistic offset data analysis and physics modelling capabilities allows to investigate historic performance, capture key lessons learned, and improve well design for field optimization.
The introduction of an innovative offset analysis functionality acted as an enabler to create situational understanding of events and implement corrective actions for new wells being planned, reducing a process that often took weeks to a few days. Enabled by effective data aggregation workflows and 2D/3D viewing techniques in multiple data domains including depth and time, an expedited understanding of well events was achieved. Offset data correlation and analysis capability was paired with physics-based drilling hydraulics modelling to resolve root causes and establish an improved risk registry.
The case example discussed in this paper illustrates a focused study of well event sequences in a field, the capturing and application of learnings, achieving coherent risk management and improvements of well design and engineering. The outcomes of the analysis and planning phase can also be seamlessly leveraged in real-time monitoring and process automation steps for maximum efficiency and minimum NPT in well delivery.
Realtime Open Hole Modeling of Equivalent Circulating Density Using High Frequency Pressure Sensors Across the Drillstring to Maximize Horizontal Drilling Capability
Authors: Arab R. , Toktabolat Z. , Anderson M. E. , Nasef M. M. 
Published: February 2024

Abstract
Wired drill pipe (WDP) telemetry that transmits downhole data at 57,600 bits per second has been successful in various applications throughout the world. This paper discusses how monitoring annular pressures at multiple locations along the drillstring helped manage wellbore stability, mitigate risks observed on offset wells, and allow the well to be drilled to total depth (TD).
Annular pressure is a direct function of the sum of the frictional forces within a circulating system. These forces primarily depend on external factors including mud density, flow pattern, mud rheology, rate of penetration (ROP) (and resultant cuttings load), hole cleaning, and effective circulating area. Hole overloading often causes excessive equivalent circulation densities (ECD) in horizontal sections due to poor cuttings transport and/or penetration rates at which flow rates cannot remove the drilled cuttings effectively.
With traditional methods, ECD is only available from the logging while drilling (LWD) tool in the bottomhole assembly (BHA). Along-string measurement (ASM) tools that provide three-axis vibrations, rotation, temperature, and bore/annular pressure measurements, coupled with the WDP's high-speed real-time network, allows to deliver an accurate understanding of actual annular pressure at different locations in the wellbore.
Multiple annular pressure measurements enabled a clearer understanding of hole loading at different locations along the wellbore and the effective cuttings transport and wellbore geometry. Bottoms up circulations in the intermediate section could have reduced circulation times when ECDs stayed low and little ECD reduction was observed. However, additional circulations were carried out when high ECDs were observed to ensure optimal hole cleaning and prevent loss circulation.
In the horizontal sections of offset wells, drilling to planned section TD was not achieved due to loss circulation caused by higher ECDs, or hole instability that caused drilling troubles such as stuck pipe. WDP enabled the view of ECDs in an intuitive manner throughout the horizontal section and at the shoe to ensure ECD limits were not exceeded.
A scalable equivalent fluid density (EFD) heat map viewer that shows the ECDs throughout the entire drillstring was deployed. True static densities can also be observed because pumping is not required to pulse sensor information to the surface. The viewer greatly enhances the intuitive understanding of the pressures and provides insight into how high weight sweeps affect the wellbore as they travel down the pipe ID and up the annulus.
Performance Improvement Through Continuous Learning – A Case Study of World's First Unified 30 and 20 Casing While Drilling Project
Authors: Soi A. , Prakash S. , Sharma R. , Zayyan M. , Narayanan S. , Herrera Gomez D. , Halma J. P. , Balen A. V. 
Published: February 2024

The objective of this paper is to present the valuable lessons learned in the world's first unified 30" and 20" non-directional Casing While Drilling (CWD) project in ONGC's Bombay High field and how the operational workflow was optimized based on those learnings to further enhance operational efficiency. The focus is on addressing the challenges encountered during the operation and sharing the corresponding solutions.
The project involved drilling six wells using CWD technology in the exploratory fields of Bombay High. Specially designed Copper-Bronze Drillable Alloy Bits (DABs) were attached to the bottom of the casing to drill and case the 30" conductor pipe and the 20" surface casing. A casing drive system provided mechanical and hydraulic energy to the assembly and DABs. Based on challenges faced and experiences gained in the six well pilot project, the operational procedures and workflow were constantly optimized to ensure continuous improvement in well delivery timelines.
The implementation of CWD technology reduced the average drilling time of the 30" and 20" sections from an average of 12.83 days to 5.95 days, resulting in significant savings of 41 offshore rig days in the six-well pilot project. The use of CWD technology eliminated the need for piling the 30" conductor casing, 17.5" pilot hole drilling, and subsequent hole enlargement to 26" to facilitate lowering of 20" casing. This improvement in the drilling efficiency allowed ONGC to meet their drilling targets, reduce NPT, and improve well delivery rate. The CWD technique also reduced the HSE exposure of the rig crew, as the risks associated with piling, higher man hours, tripping operations and manual handling of large sized casings were eliminated. The paper explains the various optimizations that were done in the process of CWD to overcome unique challenges that helped improve the well delivery timeline from average 6.25 days in the first 3 wells to average 5.65 days in the last 3 wells.
This paper provides a comprehensive case study of the world's first unified 30" and 20" CWD project in the Bombay High field. The lessons learned from addressing extremely unique challenges such as high torque, casing sinking, DAB drill-out issue, and CRT stuck issues during the operation are shared in the paper. The insights gained from this study will benefit drilling engineers, well planners, and operators seeking to implement CWD technology more efficiently, reduce NPT, optimize well delivery, and maintain safety standards.
Proactive Detection Methodology of Line Break Condition of the Pipeline
Authors: Zahrani M. , Kim H. , Alikhan A. , Al-Jubran M. 
Published: February 2024

Summary
This paper described an innovative approach of pipeline break prevention, which can be utilized in any facility/organization that would like to enhance its reliability, security, and efficiency. The basic concept of this approach can be applied not only in the petroleum industry, but extended to other industries.
In most of pipelines in industry, there is pipeline break detection alarm(s) system for emergency shutdown action(s) in case of real pipeline break condition happens. This pipeline break detection has been based upon the pipeline service condition changes, high-high flow and low pressure in general. These setting values may vary depending on site or process condition. However, since the setting values are based on worst or maximum process operating condition on flow and pressure against pipeline bursting, the pipeline break alarm(s) cannot be detected even though it actually happens at below the pipeline break condition setting values. Moreover, existing pipeline break alarming system cannot protect the break incident proactively as it is designed after the fact.
Khurais Producing Department in Saudi Aramco developed new automated logic program to proactively detect pipeline break symptom and condition by utilizing the combination of existing flow and pressure data, not by fixed setting values but by dynamic setting values, rate of changes per specific period of time, which can be defined depending on processes.
The proactive pipeline break detection will generate multiple levels of pre-alarms such as pre-warning alarm, intermediate alarm based on calculated rate of change values, and eventually pipeline break alarm with associated Emergency Shutdown event triggering actions will be made for actual pipeline break condition(s).
With this new methodology most of pipeline break symptom and condition can be proactively detected in real time basis, the fact of which will provide plant operator enough time to take necessary action before the real pipeline break happens. The cost saving with this simply but powerful feature will be huge comparing to any recovery cost caused by catastrophic pipeline break incident.