bluuebunny/crossref_metadata_2025_split huggingface dataset:
Calcium Alginate-Based Carbon Composite Adsorbents for Lean Methyldiethanolamine Reclamation: Laboratory to Pilot Scale Testing and Validation
Authors: Kannan Pravin , Priyabrata Pal , Banat Fawzi , Dara Satyadileep , Khan Ibrahim , AlJenaibi Eisa , AlAwlqi Marwan 
Published: December 2021

Abstract
Calcium alginate-based carbon composite (CAC) adsorbents have been proved to effectively remove total organic acid anions as HSS anions, metal ions, and organic degraded products from lean methyldiethanolamine (MDEA solvents) used as solvent in natural gas sweetening unit. During the material developmental phase, the CAC adsorbent was synthesized and utilized to remove various contaminants, including heat stable salts (HSS), organic degraded products, and heavy metal ions from lean MDEA using a lab-scale adsorption setup. Based on the results, a "demo-scale" fixed bed adsorption unit was designed and simulated using adsorption model to predict breakthrough behavior. In the current work, the efficiency of the CAC adsorbent in removing HSS and total organic acid anions were investigated. Analysis of treated samples demonstrated the removal efficiency of the adsorbent under plant scale conditions. Further experiments performed at lab scale indicated the effectiveness of the adsorbent in the removal of bicine from lean MDEA samples. This work provides a framework for future testing and comprehensive process performance evaluation of adsorbents for lean MDEA reclamation in actual plant conditions. A fast, simple, and reliable scale up procedure for fixed bed adsorber developed earlier was validated through this work.
A Paradigm Shift in Monitoring Pipeline Leaks - Combining Sensorless NPW with RTTM
Authors: Inbar Eran , Rowen Eitan , Motil Avi , Elkin Eitan , Tankersley Michael , Troxell Alexander 
Published: December 2021

Abstract
Leak detection solutions in pipelines use several known methods and technologies. However, each method and its underlying technology has their benefits and drawbacks. This article will present and evaluate a hybrid solution that combines two methods based on different physical measurements and quantities to ensure a superior detection probability, short detection time, accurate localization of faults, and minimal false alarm rates. In addition, this solution also features preventive capabilities by pointing out problematic areas in a pipeline that may need more attention.
The article presents a novel approach for pipeline monitoring using a combined solution with the strengths of real-time transient model (RTTM) technology and the power of next-generation fiber sensing geared towards leak detection. On top of acoustic sensing for leaks, it features continuous pipeline integrity monitoring where, using subtle characteristics of propagating negative pressure waves (NPW), pipeline sections signatures are tracked, aiming to detect changes that might expose pipeline integrity issues that can enable the operator to take preventive measures and plan maintenance events.
Such a hybrid solution, from AVEVA™ (RTTM) and Prisma Photonics (fiber sensing), will obtain higher levels of performance and reliability. In addition, such a hybrid approach responds to the increasing regulatory demand to have two continuously working solutions based on different physical measures to ensure leak detection and prevention of substance spillage.
This article intends to introduce such a hybrid solution with new applications in predictive maintenance for pipeline operators and shed more light on the benefits of such a solution facing further regulatory demands.
Analysis, Logic, Considerations, Mitigations, Risk Assessments and Procedure to Drill Horizontal Gas Well With Separation Factor Up to 0.6 between the Ellipses Avoiding Collision in Exploratory Field. Bio-UC Project at Abu Dhabi, United Arab Emirates
Authors: Ruiz Fernando , Nunez Ygnacio , Hammadi Mahra Al , Hamdy Ibrahim , Al Shamisi Eisa , Kumar Rakesh , Al Hendi Mohamed , Negoi Adrian , Hebish Ayman , Baruno Agung , Al Arfi Saif , Al Mutawa Ahmed , Coscia Marco , Goiran Pierre , El Yossef Bassem , Al Blooshi Abdulla , El-Hafez Hany 
Published: December 2021

Abstract
In a current oil & gas challenging drilling environment where the fields are becoming very congested, PAD drilling and field grid designs with close proximity wells operation is booming. Drilling challenging wells with high collision risks is common as a result of the requirement to maximize the Asset value of the oil fields. For this reason, the urge for ensuring accurate well placement is becoming critical and as a result high technology methods are required to be in place.
Developing new areas where the poor and/or inaccurate drilled wells information (most of them are vertical) affect planning and placement of new wells due to the uncertainty in existing wells trajectories, causing collision issues among the new planning and the "trajectory" of the existing wells, leaving huge quantities of reservoir volume not possible to drain. For this study case, where the reservoir has some complexity due to faults and water, such limitation is critical.
The analysis and fusion of new techniques and procedure to manage the risk for the collision were implemented. Directional tools with high level of accuracy measurements were deployed and stringent procedures are put in place.
The Analysis, Logic, Considerations, Mitigations, Risk Assessments and a New Procedure implemented to avoid collision issues while drilling horizontal wells with Separation Factor (SF) less than 2 (standard worldwide is equal or above 2 and for this case, it was 0.6). This was developed by the Biogenic / Unconventional team, Drilling Department of Abu Dhabi National Oil Company (ADNOC) Onshore with the support of drilling service company and the approval of the ADNOC Head Quarter, to take advantage of around 0.9 km2 of hydrocarbon area for future drain.
The well was drilled successfully and safely, no collision or magnetic interference issue in any trajectory survey were reported during drilling and passing close by the existing well.
Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture
Authors: Nakagawa Yujin , Inoue Tomoya , Bilen Hakan , Mopuri Konda R. , Miyoshi Keisuke , Abe Shungo , Wada Ryota , Kuroda Kouhei , Nishi Masatoshi , Ogasawara Hiroyasu 
Published: December 2021

Abstract
Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, real-time stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was developed by combining an unsupervised learning model built using an encoder-decoder, long short-term memory architecture, with a relative error function. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. An evaluation method of stuck-pipe possibilities using a relative error function reduced false predictors caused by large variations of drilling parameters. The prediction technique was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors calculated with the relative error function increased 0.5-10 hours prior to the pipe sticking for 17 out of 34 stuck-pipe events (thereby partly confirming our hypothesis).
Fugitive Emission Monitoring System Using Land-Based Sensors for Industrial Applications
Authors: Roy Arjun , Datchanamoorthy Senthilkumar , Nundy Sangeeta , Keely Bhaskerrao , Kim Okja , Chan Godine 
Published: December 2021

Abstract
Metal-oxide based emission detection sensors are typically used for point measurements of hydrocarbon emissions. They are low-cost sensors and can be used for continuous monitoring of emissions. This paper describes an analytical framework that uses time series data from a collection of such sensors deployed at a customer site, along with weather conditions, to detect anomalies in emission data, identify possible emission sources and estimate the leak rate from fugitive emissions. The analytical framework also comprises an optimization module that helps in determining the optimal number of sensors required and their potential location at a customer site. The paper discusses results of the different steps in the analytical framework obtained using concentration data generated using numerical simulations and obtained through controlled leak field tests.
Lead Application to Cure Sap Wells by Deploying Straddle Packer, Success Story
Authors: Gadelhak Abdelrahman Mohamed , Al-Badi Mohamed , Al-Bairaq Ahmed , Al Mheiri Eissa , Al-Hosani Abdullah Haj , Ahmed Zeeshan , Ahmed Sami Ullah Bashir , Ahmed Mubashir , Abedelkhalik Waleed Omar , Naser Sameh Hassan , El-Shater Haysam , Al Assar Wessam , Ross Steve , Duncan Blair 
Published: December 2021

Abstract

Objective/Scope
The Increase of inactive wells due to subsurface integrity issue is observed in brown fields, Fig-1 is, showing the record for onshore UAE asset, the economic challenges is calling for alternative solutions to restore well integrity with lower cost.
Straddle packer application is consists of two tandom packers with spacer pipe in between with anchoring system deployed riglessly in the well to isolate the communication point between Ann A and Tubing.Fig-2,

Methods, Procedures, Process
Communication between tubing and annulus A (Failure of primary barrier) is identified as the right candidate wells for straddle packer application,
First step is to clearly identify the point of communication, it has been done by annulus pressure investigation excersize during flowing and shut in condition, observing the return of annulus fluid which was the same produced gas
Noise log has been conducted and clearly identified the communication point at SPM (Side Pocket Mandrel) to be used for emergency killing,
Tubing integrity test was conducted using nippless plugs and inflow test below and above the leak point and confirm no other leak points within the tubing
Engineering drawing for the leaking assembly was reviewed to design the dimension of straddle packer assembly, length and packer size
It is recommended to deploy the assembly using electric line correlation for accurate depth selection
After setting annulus pressure observed no build up
Well opened safely to production

Results/Observation/Conclusion
Leak point arrested, well primary barrier restored
Removed from DWS (drilling and workover schedule) and restore well production in addition to improving inactive string KPI for Gas asset
Save almost work over cost for gas well XX-197

Novel/ Additive information
The way forward is to check the scalability of extending this application among other ADNOC assets and to screen the right candidate wells for this application
To add this application as a part of well integrity procedures and recommendations for such like cases
A Tool for Improving Decision Quality on Upstream Consortia
Authors: Cunha J. C. , Bickel J. Eric , Mendoza Luis , Walker Jeremy , Coopersmith Ellen , McKnight Craig A. 
Published: December 2021

Abstract
This article aims to provide a guideline to better decision quality on multi-company upstream projects. The scope is to provide a high-level overview of what should be included in a decision quality process, when companies with different levels of ownership and influence on the decision-making process naturally tend to have different approaches towards risks and decision management.
It is well known in our industry that there is a predominance of multicompany participation in these projects and the paper will provide guidelines that will ultimately provide better decision quality and participant's alignment.
Normally high-risk upstream projects have multi-company ownership. However, it has been noticed that companies tend to face decision management differently, which causes unnecessary delays on budget approvals and even operational timeline. Procedures to normalize the definition of an initial decision frame and creation of solutions based on a good set of alternatives are paramount to facilitate discussions and drive final recommendations.
In summary, companies’ alignment on decision-making processes is key to quality, speed, and effectiveness of those decisions and critical to project success. Alignment between partners on pivotal decisions can significantly improve project delivery.
The main results are practical guidelines for generating (a) decision framing, (b) strategies, (c) alternatives and potential consequences, and (d) logical analysis, partners’ alignment and commitment to action.
Field Implementation of In-Depth Conformance Gel Treatment Prior to Starting an ASP Flooding Pilot
Authors: Al-Murayri Mohammed T. , Hassan Abrahim , Alajmi Naser , Nesbit Jimmy , Thery Bastien , Al Khoury Philippe , Zaitoun Alain , Bouillot Jerome , Salehi Nazanine , Pitts Malcolm J. , Wyatt Kon , Dean Elio S. 
Published: December 2021

AbstractMature carbonate reservoirs under waterflood in Kuwait suffer from relatively low oil recovery due to poor volumetric sweep efficiency, both areal, vertically, and microscopically. An Alkaline-Surfactant-Polymer (ASP) pilot using a regular five-spot well pattern is in progress targeting the Sabriyah Mauddud (SAMA) reservoir in pursuit of reserves growth and production sustainability. SAMA suffers from reservoir heterogeneities mainly associated with permeability contrast which may be improved with a conformance treatment to de-risk pre-mature breakthrough of water and chemical EOR agents in preparation for subsequent ASP injection and to improve reservoir contact by the injected fluids. Each of the four injection wells in the SAMA ASP pilot was treated with a chemical conformance improvement formulation. A high viscosity polymer solution (HVPS) of 200 cP was injected prior to a gelant formulation consisting of P300 polymer and X1050 crosslinker. After a shut-in period, wells were then returned to water injection. Injection of high viscosity polymer solution (HVPS) at the four injection wells showed no increase in injection pressure and occurred higher than expected injection rates. Early breakthrough of polymer was observed at SA-0561 production well from three of the four injection wells. No appreciable change in oil cut was observed. HVPS did not improve volumetric sweep efficiency based on the injection and production data. Gel treatment to improve the volumetric conformance of the four injection wells resulted in all the injection wells showing increased of injection pressure from approximately 3000 psi to 3600 psi while injecting at a constant rate of approximately 2,000 bb/day/well. Injection profiles from each of the injection well ILTs showed increased injection into lower-capacity zones and decreased injection into high-capacity zones. Inter-well tracer testing showed delayed tracer breakthrough at the center SA-0561 production well from each of the four injection wells after gel placement. SA-0561 produced average daily produced temperature increased from approximately 40°C to over 50°C. SA-0561 oil cuts increased up to almost 12% from negligible oil sheen prior to gel treatments. Gel treatment improved volumetric sweep efficiency in the SAMA SAP pilot area.
Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments
Authors: Mathew Eric Sonny , Tembely Moussa , AlAmeri Waleed , Al-Shalabi Emad W. , Shaik Abdul Ravoof 
Published: December 2021

AbstractA meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results.The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.
Mooring Sense Project: A Risk-Based Integrity Management Strategy for Mooring System of Floating Offshore Wind
Authors: La Grotta Alessandro , Harris Róisín Louise , Da Costa Clive 
Published: December 2021

Abstract
While Floating Offshore Wind (FOW) represents a significant opportunity to foster wind energy development and to contribute to remarkable CO2 emissions reductions, its associated operational costs are still substantially above grid parity, and significant innovation is needed.
MooringSense is a research and innovation project which explores digitisation technologies to enable the implementation of risk-based integrity management strategies for mooring systems in the FOW sector with the aim to optimise Operations and Maintenance (O&M) activities, reduce costs, and increase energy production.
As part of this project, a risk-based assessment methodology specific for the mooring system of Floating Offshore Wind Turbines (FOWT) has been developed; this allows the development of a risk-based Mooring Integrity Management Strategy that can result in more cost-effective inspection planning. The methodology shall utilise the information made available by numerical tools, sensors, and algorithms developed in the project to update the risk level of the mooring system and set the required plan to mitigate the risk.
Leveraging the additional information from monitoring technologies and predictive capabilities to determine the mooring system condition and remaining lifetime, the strategy provides the criteria for optimal decision making with regards to selection of O&M activities.
The risk-based strategy developed allows for optimal planning of inspection and maintenance activities based on dynamic risk level that is periodically updated through the interface with the Digital Twin (DT).
The validation of the strategy will demonstrate potential cost saving and economic advantages, however, it is expected that the overall MooringSense approach can reduce FOW farm operational costs by 10-15% and increase operational efficiency by means of an Annual Energy Production increase by 2-3%.
The MooringSense project comprises of the development and validation of innovative solutions coming from multiple disciplines such as numerical modelling, simulation, Global Navigation Satellite System (GNSS), Structural Health Monitoring (SHM), and control systems which will provide valuable input to the risk-based mooring integrity management strategy.
Application of a Combination of Machine Learning and Reservoir Physics to Increase Production in a Mature Waterflood Field in the San Jorge Basin in Argentina
Authors: Gomez Javier Eusebio , Robles Marcelo , Di Giuseppe Cristian , Galliano Federico , Centineo Jeronimo , Medda Fernando , Calad Serrano Carlos Mario , Plotno Sebastian , Sarma Pallav , Gutierrez Fernando 
Published: December 2021

Abstract
This paper presents the process and results of the application of Data Physics to optimize production of a mature field in the Gulf of San Jorge Basin in Argentina. Data Physics is a novel technology that blends the reservoir physics (black oil) used in traditional numerical simulation with machine learning and advanced optimization techniques. Data Physics was described in detail in a prior paper (Sarma, et al SPE-185507-MS) as a physics-based modeling approach augmented by machine learning. In essence, historical production and injection data are assimilated using an Ensemble Kalman Filter (EnKF) to infer the petrophysical parameters and create a predictive model of the field. This model is then used with Evolutionary Algorithms (EA) to find the pareto front for multiple optimization objectives like production, injection and NPV. Ultimately, the main objective of Data Physics is to enable Closed Loop Optimization.
The technology was applied on a small section of a very large field in the Gulf of San Jorge comprised of 134 wells including 83 active producers and 27 active water injectors; up to 12 mandrels per well are used to provide with selective injection, while production is carried out in a comingled manner. Production zonal allocation is calculated using an in-house process based on swabbing tests and recovery factors and is used as input to the Data Physics application, while injection allocation is based on tracer logs performed in each injection well twice a year.
This paper describes the modeling and optimization phases as well as the implementation in the field and the results obtained after performing two close loop optimization cycles. The initial model was developed between October and December 2018 and initial field implementation took place between January to March 2019. A second optimization cycle was then executed in January 2020 and results observed for several months.