bluuebunny/crossref_metadata_2025_split huggingface dataset:
Near Bit Load Sensing in RSS Applications
Authors: Hohl Carsten , Joerns Sven-Hendrik , Mihajlovic Sasa , Roders Ingo , Wille Christian 
Published: February 2022

Abstract
Direct feedback from the drilling process will be key to enhance overall drilling performance further, improve steering and will help detect drilling dysfunctions early. This is especially true for demanding drilling applications, such as drilling in difficult geological formations. Faults, hard stringers or borehole instabilities can have a negative impact on rate of penetration (ROP) or even worse can damage equipment and cause non-productive time (NPT). This gets pronounced for extended reach drilling, where long drill strings delay the received feedback from the drilling process or effects along the drill string (e.g friction) skew the results.
To overcome these obstacles a near bit sensor was developed to provide real time weight-on-bit (WOB), torque-on-bit (TOB), bending and bending orientation data. It is directly integrated into the steering unit of the rotary steerable system (RSS) with the goal to be simplistic, cost efficient and robust. To discuss potential uses and limitations of the load sensor, the basic design and operational principle are shown.
Different usage scenarios of the system are presented in theoretical terms and backed by real-world data. One scenario is the possibility to diagnose reasons for low ROP, for instance weight transfer issues can be distinguished from bit damages if WOB and TOB real time data is available. For early detection of unwanted deflections in directional drilling, for instance due to faults or tilted beds, near bit bending and bending orientation measurements of the RSS system can be used.
The near bit sensor design proves to be reliable and cost efficient. It shows high potential to increase ROP and reduce NPT and overall drilling cost.
Identification of Key Wells for Waterflood Optimization Considering Geologic Uncertainty
Authors: Sen Deepthi , Chen Honquan 
Published: February 2022

AbstractStreamline-based rate optimization is an iterative process, requiring several simulation runs. Though efficient for a single realization, it can be prohibitively expensive while considering geologic uncertainty involving large number of realizations. Moreover, the optimal schedule based on one individual geologic model may not necessarily result in favorable outcomes for the real field due to the geologic inconsistencies between the real field and the model. This paper proposes a workflow that integrates unsupervised machine learning and streamline techniques to select representative geologic realizations based on their flow features. The proposed approach generates a distribution of optimal rates for each well, and this in turn is used to identify key wells for which we may advise rate change with high certainty.Given a set of historical production and injection data, firstly, an ensemble of Nreal history-matched geologic realizations is generated using ensemble-smoother with multiple data assimilation (ESMDA). Subsequently, the streamline time-of-flight (TOF) and principal component analysis (PCA) are used to extract the flow feature of all realizations, based on which k-means clustering algorithm generates a subset of Nclust realizations representing the whole ensemble. The rate optimization is performed on each of the representative realizations using a streamline-based rate optimization algorithm that seeks to maximize the oil production during the optimization period. The distribution of optimal schedules obtained by optimizing the representative realizations is shown to be in high correspondence with that obtained by optimizing the full ensemble. Using the optimal schedule distribution, the key wells are identified, for which rate change is advised with high certainty. The workflow is tested on a synthetic 2D reservoir model as well as a 3D field-scale benchmark reservoir model (SAIGUP model).The novelty of this work is the combination of the streamline-extracted flow features and unsupervised machine learning methods to formulate an efficient workflow for uncertainty analysis of optimal well schedules. The proposed approach ensures quality and rigor of uncertainty analysis with significantly reduced number of geologic realizations and thus, makes the approach well-suited for large scale field applications.
Qualifying Novel, Risk Reducing and Efficient Disruptive Definitive Dynamic Surveys in Middle Eastern Fields
Authors: Oliveira Victor , Dossary Abdullah , Osman Ahmed , Elsadig Mohamed 
Published: February 2022

AbstractOver the past 40 years, the oil and gas industry standard for performing wellbore surveys with measurements-while-drilling (MWD) tools was by six-axis stationary surveys. These surveys required a sequence of events involving releasing drillstring torque and pressure and providing zero motion while making and sending downhole survey measurements. This procedure involves drilling risks, delivers reduced drilling efficiency and increases the risk of differential sticking in depleted reservoirs.As a leader in modernizing the drilling industry, a major operator has worked with a major directional service provider to pioneer a new MWD surveying technology that delivers definitive surveys while drilling and without any need for stationary time or the risks associated with it. The breakthrough technology is expected to change the previous 40 years of industry practice with a new MWD surveying technology known as definitive dynamic surveys (DDS) technology. The technology will deliver definitive surveys while on bottom drilling with full parameters and complete data quality control. This technology will have the benefit of eliminating the current time needed for stationary surveys, reducing the differential sticking risks, increasing surveying frequency for better wellbore positions and providing improved wellbore tortuosity understanding to manage microdoglegs for improved wellbore quality and completion operations integrity.As a main field tester of this technology, over 30 test runs were made within Middle East fields. As the technology matured, three operational runs have been recently conducted with the aim of comparing and qualifying them compared to industry standard gyro and MWD surveys. The operations were conducted in different well profiles including a vertical, a highly deviated, and a horizontal well. The results have shown an excellent match with less than 0.3° in inclination and 0.5° in azimuth and well within the bounds of their respective error models. The data also showed the improvement in well positioning and drilling efficiency using this technology. Finally, the technology has shown around 50% reduction in connection time on the trial wells.These results have given the required confidence for the technology, gaining the benefits this disruptive technology brings to the drilling process. This technology is expected to change the industry norm of wellbore surveying and produce a disruptive change in the drilling and well placement operations.
How Data Product and Mesh Thinking can Accelerate Your Digital Transformation Strategy and Adoption of the OSDU Data Platform
Authors: Quinn Paul 
Published: February 2022

Abstract

The Drive for Digital Transformation
Today's Energy Industry has a multitude of challenges – from ensuring that existing assets are used to their maximum potential to incorporating new ways of working and New Energy sources whilst adapting to and adopting constantly changing regulatory and environmental regulatory concerns. This is on top of the traditionally strong concerns of operating at scale, safety and the use (and reuse) of assets that have been established over a significant period of time. This is in the midst of a transformation happening across all of industry. The new industrial revolution in data, analytics and AI has meant that there is both significant opportunities and pressure to address these challenges and concerns using new and novel, tools, technologies and techniques. From a company perspective this means that there may be a focus within these new areas to attempt to deliver increased efficiency, new information or increased value. However, underpinning this there must be a focus on ensuring that foundational capabilities are in place to provide the safe, reliable and agile environment in which this digital transformation can occur.
There are a number of ways of thinking about data and its use within an organisation that may make the goals and expectations of a digital transformation programme more achievable. This is both based on emerging and established best practises for using data within the IT development community, which focus on how data must be protected and developed as a valuable and, crucially, usable asset within a company.
Within systems projects there is also the approach of reusing proven, field tested systems and components (COTS). Through the reuse of these Energy Industry building blocks, operators can focus instead on more company-specific value-adding digitalisation activities and attempt to move up this value chain by looking towards efforts that enable innovation or provide competitive advantage instead of duplicating effort where there exists coordination (or a de facto standard) across the industry.
The OSDU Data Platform cross Energy Industry collaborative initiative to deliver foundational data capabilities and also the provision of common data definitions, ways of working and common functionality on top that data for multiple disparate domains in the industry. This will allow operators, system integrators, independent software vendors, and application developers to work with data in a consistent manner and give this ecosystem the ability to build value adding tools, platforms and apps against. The OSDU Data Platform can therefore deliver key capabilities within a digital transformation roadmap and allow operators to focus on differentiating capabilities. This paper explains, from a high level, how the OSDU Data Platform can be used in conjunction with IT best practices to accelerate digital transformation.
Role of Lean Principles and 5S Programs in Increasing Maintenance Productivity
Authors: Al-Hussain Abdullah , Al-Ahmed Hussain 
Published: February 2022

Abstract
Lean is a methodology that concentrates on monitoring a process or a system, analyzing it, minimizing waste within the system, and increasing productivity simultaneously. On the other hand, 5S, which is an abbreviation for Sort, Set-In-Order, Shine, Standardize, Sustain, is a principle utilized to eliminate Process wastes which may include: wasted time, wasted space, and wasted inventory. The scope of this paper is to showcase the importance of employees’ contribution in continuous improvement initiatives and culture of capitalizing on the benefit of lessons learnt and knowledge sharing.
The program began with educating maintenance team about Lean Principles, 5S Programs, DMAIC (Define, Measure, Analyze, Improve and Control) methodology and their importance. Then, sessions of brainstorming and data collection were conducted. In addition, employees were encouraged to use A3 papers to draft a comparison between current state and future state of any process, step, or a procedure which they think can be performed in a better, faster, safer, and more efficient manner. Next, a weekly continuous improvement meeting was held by quality committee which consisted of members from maintenance, quality & reliability, and management teams to review each improvement idea and take a proper action.
The positive impact of Lean, 5S and DMAIC have been proved working and helped drilling business unit to exploit cost saving opportunities, maintain a perfect record of service quality with client, and improve maintenance productivity. This includes 100% client satisfaction and operating efficiency, 50% reduction in equipment turnaround time, over 100% increase in number of equipment maintained per month and decrease in overtime man-hours by 90%. These results were achieved after close monitoring and controlling of the overall process conducted for over 52 weeks (From Q3 2020 to Q3 2021) and more than 60 improvement ideas implemented. Improvement ideas were received, reviewed, analyzed, further enhanced, and immediately implemented to ensure the end results are obtained. The improvement ideas and lessons learnt register was built to document, give credit, and protect intellectual property of the idea owner and recognize top contributors to the register.
This success story of performance elevation, cost reduction, process optimization and best practices implementation can be cascaded to other departments and companies delivering products and services to the petroleum industry.
AI Driven Approach to Predict Sonic Response Utilizing Typical Formation Evaluation Logs
Authors: Alghamdi Muataz 
Published: February 2022

Abstract
Artificial intelligence is used to capitalize on commonly run well logs to build and train an artificial neural network model to predict the sonic log curve. The dataset used to develop the model is made up of a total of 50 well logs for an oilfield that is located in the eastern part of US. It includes actual recorded sonic logs, which allows a comparison between model driven results and actual sonic curves to evaluate accuracy.
Commonly run logs are used to train prediction models. Log selection targets meaningful petrophysical correlations to sonic properties. Well logs were preprocessed for data cleanup and scaling. After that, the data was broken down to geologically continuous chunks, and splitted to training, validation, and testing subsets. Artificial neuronal network models that use different combinations of neural network layers, hyperparameters, optimizers, and architectures to train the model are investigated to decide on the best approach to construct the predictive model. Automated hyperparameters tuning is employed to further enhance the model accuracy.
After evaluation, a total of four models are selected to train. The resulted sonic logs produced by the models are assessed for accuracy, and are visually plotted and compared to actual logs. The models are ranked based on their accuracy and ability to detect geological features. The models show variations in prediction capabilities. They show that models that are built based on recurrent neural network performed the best. In addition, introducing convolutional neural layers to the models further enhanced accuracy. It is observed that the models are doing a better job of predicting when there is a clear change in values, and are less effective when actual values rate of change is low. Overall, the model that is built employing Gated Recurrent Unit combined with convolutional layers performed the best. In comparison, models that are purely built on dense neural network structure didn't yield good results. Also, it is proven that automated hyperparameter functions are effective in improving models’ accuracy by tuning of parameters through several iterations.
The paper exhibits the effectiveness of utilizing evolving AI techniques to capitalize on expanding existing formation evaluation methods to extract more data from commonly run well logs. In addition, developing a model to predict sonic response negates the need to run actual sonic logs, which saves cost and operational time. Also, AI derived data from previously acquired logs in the wells of interest can help in calibration of models in applications such as geomechanics and wellbore stability studies. This approach can be potentially used for real-time predications without running sonic tools in wells.
A Non-Intrusive Method Using Positively Induced Pressure Waves to Track Moving Objects Within Pipelines and Wellbores
Authors: Newman Stephen , Jack Graham , Savenkova Anna 
Published: February 2022

Abstract
A field proven method is presented for live location tracking of objects within pipelines and wellbores. Tracking is based on analysis of induced pressure waves within the conduit. The method can be used, for example, to track the location of a pipeline pig during a pigging campaign. Data such as pig position, velocity and estimated time of arrival can be calculated. The method can also be used to confirm an object has not become stuck.
A high sensitivity pressure recorder is attached to the pipeline or wellbore of interest. A pressure wave is then formed by releasing a quantity of high pressure gas or liquid into the pipe. The wave then travels at the speed of sound along the pipe and reflects off any object forming an obstruction. Once reflected the pressure wave returns to the pressure recorder. Through analysis of the time this pressure wave takes to travel along the pipeline and return to the data recorded, the distance to the object can be calculated. The process is repeated to provide live tracking.
The method discussed has recently been successfully used to track pipeline pig location on a large diameter pipeline project in the middle east. The pipeline had a stuck pig from recent commissioning activities. The location of the pig was unknown. It was also unknown if the pig was stuck or still moving slowly.
Pressure logging instrumentation was mobilized to site and from the initial surveys performed, analysis of the recorded pressure data confirmed the location of the pig. Subsequent surveys and analysis a short time later confirmed the pig was not moving. A remediation plan was put in place to retrieve the pig and pressure wave technology was used to track the location of the pig during its recovery.
During the operation it was found and validated that it was possible to track the location of the pig to within 100m. Through successive testing, the live pig velocity was calculated and from this an estimated time of pig arrival. Throughout the entire pigging operation, the pig location was tracked. The pig arrived in the pig receiver within minutes of the predicted arrival time.
Live tracking provided confidence to the pipeline operator that the remediation plan was going to be successful. Accurately understanding the pig arrival time allowed optimization of subsequent commissioning activities.
The proposed method allows tracking from one end of pipeline. Sensors are not required along the route and a transmitter system is not required on the pig. The method can be applied to any object in a pipeline or wellbore and offers savings to operators. Information collected can also be used to better plan subsequent activities and contingencies creating savings on overall project schedule.
Multi-Scale Investigations on the Geometries of Hydraulic Fractures in Conglomerate Reservoirs
Authors: Liang Haipeng , Tang Huiying , Qin Jianhua , Li Yang , Zhou Xianmin , Zhang Liehui 
Published: February 2022

Abstract
Currently, the research on hydraulic fracture geometries is mainly focused on tight sandstone and shale. The investigations on the conglomerate tight reservoirs, e.g., Mahu Oilfield in Junggar Basin, China, is still lacking due to its uniqueness and late discovery time. The strong heterogeneity and the existence of gravels in conglomerate tight reservoirs put great challenges on the study of hydraulic fracture geometries. In this paper, a whole field cohesive zone model in finite element method is used to model the fracture nucleation and propagation in rock matrix (sand) and gravels in lab scale. The numerical model is validated against some published experimental results. Based on the analysis of numerical results, a mathematical model for quantitative characterization of fracture growth speed in conglomerate reservoir is proposed. This model is critical to connect the fracture propagation behaviors in lab-scale with the hundreds of meters field-scale hydraulic fractures. For the field scale fracturing simulations, the UFM (unconventional fracture model), which is based on boundary element method, has been widely used. Considering the similarity of crossing behaviors between hydraulic fracture-gravels and hydraulic fracture -natural fractures in conglomerate and shale respectively, a series of natural fractures are used to equivalent the impact of gravels in lab scale in the field scale simulations. The parameters of the equivalent natural fractures are determined according to the propagation model extracted from the lab-scale numerical simulations. The multi-scale research on fracture geometries and methods for field scale fracturing simulations for Mahu conglomerate reservoir could provide important guidance for the future design and optimizations of hydraulic fracturing.
Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers
Authors: Muhammad Moin , Ratajczak Todd , Watson Nathanial , Zhu Jie , Little Paul 
Published: February 2022

Abstract

Objective
Use of near-infrared (NIR) spectroscopy for Reid Vapor Pressure (RVP) measurement has enabled condensate stabilizers at natural gas separation sites to optimize their operation, resulting in significantly lower energy usage, less gas production, more liquid conserved, more on-spec product, and higher profitability. The combination of energy usage optimization and an improved liquid/gas ratio provides a double benefit in both Environmental, Social, and Governance (ESG) criteria and return on investment (ROI). Optimization is achieved by utilizing the 15-second measurement interval of the JP3 Verax NIR on-line analyzer for process control, as compared to manual control. In condensate stabilizers, RVP is reduced by removing light components (C4-: butanes and lighter hydrocarbons) by heating; these are sent to a compressor for vapor recovery. In many instances, the light hydrocarbon gas is flared off when it is uneconomical to transport it for sale. The remaining condensate goes to a reboiler and a cooler before being sent to pipeline. The reboiler heats the liquid fraction before recirculating it to the column; the cooler uses pumped water in a heat exchanger to reduce the stabilized condensate temperature and vapor pressure for storage and transport.
These are energy-intensive processes – the compressors use electricity, the reboiler burns fuel gas, the coolers use electricity and large volumes of water, and waste gas flaring impacts carbon credits and allowances. Optimizing these processes via real-time process control informed by RVP measurement can provide substantial benefits in each energy-consuming step.