bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bytes to Barrels: Analogue for Real Time Drilling Assistant
Authors: Sharma Ayush Sharma , Raj Kriti Raj , Patidar Atul Kumar 
Published: November 2020

Abstract
Interpretation of subsurface lithology has been an integral part of bottom-hole investigation while drilling a well for hydrocarbon exploration. Conventional methods such as mud logging and open-hole logging have proven to be effective in this aspect. But there arises a need of the real time interpretation of subsurface drilling lithologies to have a better control over desired well path and economics, which is very limited through available conventional methods. This paper aims to provide a classifier that can deliver a justified chance of success in lithology estimation based on input matrix provided to the classifier. The dataset is taken from open source platform (Equinor), which provided the drilling history of 15_9_F_15_D well of North Sea Basin along with the interpreted lithology. This dataset is used to prepare the input matrix and the corresponding output used for the classifier. It incorporates seven drilling parameters, i.e. rate of penetration (ROP), weight on bit (WOB), bit rotation per minute, total bit rotations, corrected drilling exponent, mud flow & mud weight and the corresponding recorded subsurface lithology for each foot. It is made sure that the chosen drilling parameters have a direct impact on the rate of penetration with changing physical characteristics of the formations being penetrated. The formations are calibrated with the integer values from 1 to 5 for easier numerical computations. The prepared dataset is divided into two components i.e. training Set (80%), and test Set (20%) with feature scaling implemented. The most intuitive classification model among all the tested classification algorithms i.e. multiple classification algorithm, artificial neural network, and KNN search method is stated based on their individual F (1) score on the test set. It is observed that the cost function for the multiple classification model stopped reducing after the first few iterations irrespective of learning rate value alterations, which induced a limit on the accuracy provided by the chosen algorithm. It is observed that the performance of ANN is quite good for the lithology present in majority, such as claystone. However, the performance degrades exponentially for the lithologies occurring in minorities such as sandstone, dolomite, marl and limestone. The results obtained from KNN classification model not only provides an accuracy of 95% in claystone, 92% in sandstone and 89% in marl which makes this model the most appropriate classifier to be used among the three.
The preliminary results, through the comparison among the three classifiers not only provides the most intuitive model for subsurface lithology prediction using real-time drilling data, but also gives an idea about the most appropriate classifier to be used in the future reference, be it any basin, given that the same set of drilling parameters are used as the input matrix for training the classifier.
Enabling Subsea Well P&A with Modern Vessels
Authors: Lim Tze King , Tellier Elizabeth , Campbell Mike 
Published: November 2020

Abstract
An increasingly common problem faced by subsea well operators is plugging and abandonment (P&A) of wells using modern drilling vessels. This challenge is due to older wellhead and conductor designs which are not fatigue resistant and which have less bending load capacity. Combined with the current trend of using larger and heavier BOP stacks provided by 5th and 6th generation drilling vessels, this results in large motions and loads transferred to the wellhead, and hence higher risk of fatigue failure or component capacities being exceeded during P&A. This paper discusses the methodology for assessing the feasibility of performing P&A on wells with modern vessels and evaluates mitigation options available.
Firstly, analysis is performed to determine historical fatigue accumulation in the wellhead and conductor system from drilling and completion operations. Historical fatigue accumulation can be refined by incorporating details from the operational history including vessel heading, metocean conditions, and riser configuration. Fatigue accumulation from planned P&A operations with modern day drilling rigs is then assessed to determine if there is sufficient remaining fatigue margin for the planned P&A operations. If required, mitigation measures such as optimizing riser tension and vessel heading, reducing BOP stack size, calibrating analysis models using monitoring data and using a BOP tether system are evaluated. Strength assessment for P&A operations is also performed to determine vessel station keeping requirements. Examples from a number of case studies of recent subsea well P&A in Australia are presented.
The effectiveness of different fatigue mitigation measures are compared. Findings presented in the paper allow operators to efficiently evaluate and plan safe P&A operations on older wells with modern drilling vessels.
Matrix Water Injection – A Novel Technique
Authors: Eshtewi Abdullah , White Richard , Campbell Stewart 
Published: November 2020

Abstract
The Greater Enfield Project (GEP) is a challenging offshore oil development, designed to produce from the Laverda Canyon, Cimatti and Norton over Laverda oil fields. Six water injection wells are required to provide pressure support and sweep oil to three production wells in the Laverda Canyon and Cimatti oil accumulations to improve oil recovery. The GEP injection wells are a critical aspect of water flood design in a complex field, new to Woodside and with limited global benchmarks.
A specific drilling and completion fluid system (Reservoir drilling fluid, completion fluid and chemical filter cake breaker) combined with a unique clean up and displacement technique have been adopted to provide high and sustainable matrix injection performance. While filter cake breakers have been previously used in the industry, they are typically combined with a flowback for filter cake removals. Filter cake clean up by means of flowback was discounted for GEP due to cost and the inability of some wells to naturally flow during early life.
All GEP injection wells were completed in 2018-2019, one of which is globally the longest horizontal water injection well completed to date based on the Rushmore data base. Fieldwide injection commenced in July 2019 with favorable results. This paper summarises the key design aspects adopted to deliver successful matrix injection performance, presents the improvements implemented during offshore execution and provides an insight into the early life injection performance.
Using Data-Mining CRISP-DM Methodology to Predict Drilling Troubles in Real-Time
Authors: Al Gharbi Salem , Al-Majed Abdulaziz , Abdulraheem Abdulazeez , Patil Shirish , Elkatatny Salaheldin 
Published: November 2020

Abstract
Drilling is considered one of the most challenging and costly operations in the oil and gas industry. Several initiatives were applied to reduce the cost and increase the effectiveness of drilling operations. One of the frequent difficulties that faces these operations is unexpected drilling troubles that take place and stops the operation, resulting in losing a lot of time and money, and could lead to safety issues culminating in a fatality situation. For that, the industry is in continues efforts to prevent drilling troubles. Part of these efforts is utilizing the artificial intelligence (AI) technologies to identify troubles in advance and prevent them before maturing to a serious situation. Multiple approaches were tried; however, errors and significant deviation were observed when comparing the prediction results to the actual drilling data. This could be due to the improper design of the artificial intelligent technology or inappropriate data processing. Therefore, searching for dynamic and adequate artificial intelligent technology and encapsulated data processing model is very essential.
This paper presents an effective data-mining methodology to determine the most efficient artificial intelligent technology and the applicable data processing techniques, to identify the early symptoms of drilling troubles in real-time. This methodology is CRISP-DM that stands for Cross Industry Standard Process for Data Mining. This methodology consists of the following phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. During these phases, multiple data-quality techniques were applied to improve the reliability of the real-time data.
The developed model presented a significant improvement in identifying the drilling troubles in advance, compared to the current practice. Parameters such as hook-load and bit-depth, were studied. Actual data from several oil fields were used to develop and validate this smart model. This model provided the drilling engineers and operation crew with bigger window to mitigate the situation and resolve it, prevent the occurrence of several drilling troubles, result in big time and cost savings. In addition to the time and cost savings, CRISP-DM provided the artificial intelligent experts and the drilling domain experts with a framework to exchange knowledge and sharply increase the synergy between the two domains, which lead to a common and clear understanding, and long-term successful drilling and AI teams collaboration.
The novelty of this paper is the introduction of data-mining CRIPS methodology for the first time in the prediction of drilling troubles. It enabled the development of a successful artificial intelligence model that outperformed other drilling troubles prediction practices.
Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models
Authors: Browning James , Gorell Sheldon , Andrews Justin 
Published: November 2020

Abstract
Design, evaluation and approval of a field development plan is generally very iterative, requiring meetings, design and execution of simulation models, then financial reviews, back to updating and running the models, then financial evaluations again, and on and on. This paper presents a system (tools and processes) designed to significantly reduce the iterations of simulation runs and financial reviews. This is accomplished through implementing financial logic directly inside the model. This novel ability assesses and controls individual well operations every time step, eliminating the need to modify (and rerun) a model after every run due to financial review indicating a well is losing money and needs to be shut in or modified. Even with the simple model presented, with 23 wells, at least one new iteration would be required every time post run financial analysis indicates a well's operation needs modification due to its financial performance. This would require a minimum of 23 additional runs because changing a well will also impact the performance of other nearby wells.
The system presented also provides the flexibility to configure multiple parameters into the model, enabling the automatic execution of multiple combinations of operational and financial scenarios. Such a capability enables the rapid analysis and review of parameter impacts resulting in the confidant selection of the best operating for the organization.
The impact of financial realities is demonstrated through the simulation of operating five different field scenarios, each with its own combination of producers and injectors. A variety of operational and financial parameters were included. The results indicate that including financial parameters and decisions inside the simulation run significantly improves the usefulness of the production forecast and provides considerable efficiencies in the modeling and analysis processes.
Using Digital Twins for Condition Monitoring of Subsea Mechanical Equipment
Authors: Holberg Geir Ivar , Grennberg Viktor , Martens Joakim 
Published: November 2020

Abstract
The focus on digitalization and how to reap the benefits from it, is increasing in the oil and gas industry. To make this happen subsea by using machine learning and neural networks on mechanical products, we need to understand how we can digitize them.
In this paper we will show how we can generate a true digital twin that could enable the step change in how we monitor and understand the mechanical products placed out of reach for normal preventive maintenance.
We look at the industry’s current information philosophy regarding mechanical products; from idea to development and testing to installation and operation. Where the products are currently designed to end up with a nominal 3D model, test components and the verification that they are fit for purpose is made through a test scope simulating certain scenarios defined by the industry. The products are then installed, and their condition is assumed to be fine until evidence of the opposite is apparent, and the time window to perform mitigative intervention is gone.
Creating true digital twins of mechanical products will require more data. In recent tests we have focused on generating data points to understand the response/behaviour of the products in multiple scenarios and utilized this data to describe the behaviour accurately and numerically; from this we can generate a digital representation of what state the products are in.
The results are based on recent work in developing a digital twin for mechanical products. In this work we have proven through documented testing and quantitative analysis that we can generate a validated numerical model for mechanical products. This will form the basis for understanding the state of the products and predict when intervention/maintenance is imminent for the operator. Using this model and method, condition monitoring of the mechanical products can now be enabled with relative few datapoints extracted in time intervals, and through the use of its numerical representation, establish an actual condition history of the product itself.
Further this model would enable a deeper understanding of the actual operational effects, to which the product is exposed during its lifetime, leading to more precise and cost-efficient industry requirements and system knowledge.
Carnarvon Basin: Mungaroo Formation, The Challenges in Estimating Permeability
Authors: Pillai Paul , Shams Asghar 
Published: November 2020

Abstract
There was always the challenge to match Permeability estimated from logs to Permeability derived from well tests. The main reason for this is the difference in scale, both vertically in the borehole (net contributing sands) as well as radially out into the formation. The well test that takes into consideration many hours of fluid flow into the borehole was always deemed more representative than permeability derived from logs that measures a smaller area and volume. However, this perception should not relegate log-derived permeability to an insignificant parameter within dynamic models. When wells are stimulated prior to well test (e.g. under-balanced perforation or chemical stimulation for clean-up), it is expected that the permeability from the well test be enhanced as seen in Iago-2 and Gorgon-3 wells.
This paper takes core data from wells in the Carnarvon Basin, create log relationships to predict permeability that match these core data, and compare these to wireline formation tester mobility and to well tests. The results are very promising, and the workflow proposed can be applied to any well in the basin. One of the objectives of this paper is to create a workflow that can be replicated easily and to use raw logs that are available across all wells, in order to reduce the uncertainty in the predicted permeability.
The reservoir sands of the Mungaroo formation are easily recognised by the cross over in the neutron-density logs, in both gas zones and water zones. This is the criteria used by operators to obtain formation pressure tests (MDT/RFT) and this is the same criteria used in this paper to define reservoir sands. Only those core data acquired in reservoir sands are used as the "Learning" dataset to predict permeability. Several learning datasets were created, and these were blind tested on other wells that were not part of the learning dataset. The results of these predicted permeabilities were cross plotted against core permeability that have been over-burden corrected and depth shifted to wireline logs. Where the match is not satisfactory, new learning datasets are derived and this step of the workflow is repeated. At the end, there are four groups of learning datasets that are used as the final results.
These four groups of datasets are associated with four sets of equations and these provides a very good match between the predicted log-derived permeabilities and core plug permeabilities. When compared to mobilities from formation pressure testers, the predicted permeabilities are a very good match. These were then compared to well test permeabilities showing an overall good match. This gives confidence that the predicted permeabilities from the four sets of equations are good and can be applied to any new well targeting the Mungaroo Formation in the Carnarvon Basin.
Approaching the Necessity of Pediatric Palliative Care in Colombia: A challenge for the Health System. A Retrospective Cross Sectional Study.
Authors: Villegas Susana Orrego , España Karine Maria Posada , Flores Silvia Librada , Solano Juliana Lopera 
Published: November 2020

Abstract
Background: Pediatric Palliative Care (PPC) focuses on achieving quality of life and control the symptoms for children during the sickness process, also provide support to the family in order to relief suffering and give response to needs assessment. In Colombia, this subspecialty does not exist, as a result of not having clinic practice guides and information about the population who benefit under 18 years of age. This study is to approach the necessity of PPC in our country, defining a methodology to characterize and estimate prevalence of pediatric population who may need special care in order to develop an attention model. Methods: A retrospective descriptive study was conducted among 80,926 members between 0 and 18 years of age who has a private health insurance in Colombia between January 1, 2016 and December 31, 2018. Data was obtained from the insurance transactional database. Patients were grouped based on the Association for children with life threatening conditions and their families (ACT) of the Royal College of Pediatrics four classifications plus perinatal group. Results were processed and analyzed using descriptive statistical functions in Microsoft Excel. Results: 1,694 patients (2,09%) were eligible to enter a PPC program according to their IDC-10 diagnoses based on ACT plus Perinatal Group classification. Approximately 54% were male, 57.97% were considered early childhood, 26.56% childhood and 15,47% adolescence. With regards to ACT classification and Perinatal Group; 55.55% of patients had diagnosis from Perinatal Group (55.55%) followed by group 4 (23.2%) and group 1 non-cancer (9.68%). Conclusion: Being able to identify patients in need of PPC programs early may increase it being utilize at the time of diagnosis and not wait till the end of life. This might also help alleviate misconception of PPC only being offered during the end of life period. This study should help with the development of public and private politics aimed to promote the creation of PPC programs in developing countries.
Immunomagnetic separation is a suitable method for electrophysiology and ion channel pharmacology studies on T cells
Authors: Tajti Gabor , Szanto Tibor Gabor , Csoti Agota , Racz Greta , Evaristo César , Hajdu Peter , Panyi Gyorgy 
Published: November 2020

Abstract
Background:Ion channels play emerging role in the physiological and pathological function in many cell types, including the non-excitable immune cells. As immune cells represent a heterogeneous population with considerable functional diversity, subtype-specific functional studies, such as single-cell electrophysiology of ion channels (patch-clamp), require proper subset identification and separation. Magnetic-activated cell sorting (MACS) techniques provide an alternative to fluorescence-activated cell sorting (FACS), however, the potential impact of MACS on patch-clamp experiments were not studied yet. Among others, the presence of MACS-related beads on the cell surface may alter the biophysical and pharmacological parameters of the ion channels expressed in the membrane. This motivated our experiments to describe the feasibility of MACS for electrophysiology studies on immune cells. We examined the biophysical and pharmacological parameters of the voltage gated Kv1.3 K+ channel in activated CD4+ T-cells as well as the membrane capacitance following immunomagnetic positive separation, using the REAlease® kit. This kit allows three experimental configurations: bead-bound configuration, bead-free configuration following the removal of magnetic beads and the label-free configuration following removal of the REAlease® complex congaing the antibody fragment that recognizes CD4. As controls we used FACS separation as well as immunomagnetic negative selection.Results:We found similar purity and cell viability using FACS or MACS-based positive (REAlease®) and negative selection approaches. The membrane capacitance and of the biophysical parameters of Kv1.3 gating, voltage-dependence of steady-state activation and inactivation kinetics of the current were oblivious to the presence of the beads or any components of the REAlease® kit on the cell surface. We found subtle differences in the activation kinetics of the Kv1.3 current in cells obtained using various cell isolation configurations that could not be explained by the presence of MACS-related compounds on the cells. Neither the equilibrium block of Kv1.3 by TEA or charybdotoxin (ChTx) nor the kinetics of ChTx block are affected by the presence of the magnetics beads on the cell surface. Conclusions:Taken together our results support, that MACS is a suitable method for studying ion channel in non-excitable cells, such as T-lymphocytes with the benefit of easy application and relatively low instrumentation costs.