bluuebunny/crossref_metadata_2025_split huggingface dataset:
Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence
Authors: Silvia Shejuti , Prior Malcolm , Gilad Yocheved 
Published: April 2022

Abstract
Unplanned Electrical Submersible Pump (ESP) failures can have a profound impact on well production and overall field economics; therefore, it is important to reliably predict the onset of damaging operating conditions and take proactive action to prevent early failure of an ESP. Most operators rely on production engineers to monitor and optimize equipment performance with remote monitoring and surveillance technologies that provide threshold-based diagnostics to detect electrical or mechanical problems. Due to inherent limitations in exception-based monitoring, it is extremely difficult to reliably predict the root cause of equipment failure and remaining useful life (RUL) of an ESP.
A major operator in the Americas was experiencing similar operational challenges in three of its producing assets. Unplanned failures happen frequently since they are hard to predict ahead of time with the data available to production engineers. This case study demonstrates how Advanced ESP Predictive Failure Analytics (PFA) technology has helped this operator to detect such events and extend ESP run life.

Methods, Procedures, Process
PFA is an innovative technique that leverages artificial intelligence (AI), life data analysis, physics, and knowledge-based methods to predict electrical and mechanical events and provides an estimate of RUL of an ESP. The data-driven models are trained using sensor time-series of historical failures and entail advanced data processing, interpolation, quality evaluation and feature engineering. The trained models are deployed to predict short term damage events that may lead to immediate failure, such as broken shaft, short-circuit, grounded downhole sensor failure, as well as long term events which build up over time, such as sand, and scale deposition.

Results, Observations, Conclusions
For one ESP, PFA detected scale deposition and predicted a sharp decline in RUL. After confirming the decrease in production fluid, and other surface and downhole sensor trends indicating scale deposition in the ESP, the production engineer applied chemical injection and avoided the failure. For a second ESP in this case study, PFA detected a grounding condition and predicted sudden decline in RUL. The production engineer noticed motor amps increased beyond the recommended threshold and performed electrical optimization to reduce motor amps. The ESP ran for another year and eventually failed due to grounded downhole sensor failure, which PFA had detected two weeks prior to the failure.
The New ISO/API P-Y Curves in Clays and their Reconciliation with the PISA Framework
Authors: Jeanjean Philippe , Zakeri Arash , Zhang Youhu , Andersen Knut H 
Published: April 2022

Abstract
ISO will soon issue the Draft International Standard (DIS) ISO 19901-4 to its members for comments and ballot. This DIS document includes major updates to the present state of the practice, including new p-y curves that will replace the long-standing API Matlock p-y curves for clays. The new p-y framework for offshore piles and well conductors results from comprehensive and extensive research that has successfully been tested against numerous field load tests and monitoring data. It covers monotonic, cyclic and fatigue loading conditions and uses fundamental soil properties to generate the p-y curves applicable to the loading scenario.
The release of this DIS document coincides with a significant industry momentum to develop offshore wind energy, which often includes the use of large diameter monopiles. Significant research, including the PISA project, has been conducted to provide design guidance for the offshore wind sector.
This paper achieves two objectives:
First, it presents a detailed discussion and context behind the exact formulations of the new p-y framework as it appears in the ISO DIS document, without repeating the underlying development, calibration and verification processes that are already published. Second, it demonstrates that the new ISO p-y curve framework and the PISA distributed lateral load resistance curves in clays are entirely consistent with each other. This is done by de-normalizing the published PISA curves for the Cowden site, re-normalizing them in the ISO format, and comparing the bearing capacity factors and the shape of monotonic p-y curves obtained with the two methods for the Cowden site conditions.
The second objective is an important factor in understanding the overlap and alignment between the techniques used in the offshore oil and gas sector and those used in the offshore wind sector.
From Model to Reality: Practical Insights from Mooring Integrity Monitoring Using Vessel Position Data
Authors: Jayasinghe Kanishka Milinda , Thomas Clare Victoria , Kilner Andrew Allan , Eassom Adrian Conrad , Gumley Jonathan Matthew 
Published: April 2022

Abstract
Safe and reliable access to offshore energy, whether it be through hydrocarbon or renewable sources, requires offshore infrastructure held on station by well designed, well installed and well monitored mooring systems. The bounds of what is possible with mooring integrity monitoring technology has expanded in recent years, providing stakeholders with a more complete view of risk over the life of the floating facility. As regulatory requirements eventually follow from these innovations, there is also increasing pressure on operators to expand their monitoring capabilities to retain compliance.
This paper presents an overview of some of the insights gained from using Global Navigation Satellite System (GNSS) vessel position data and metocean data for real-time mooring integrity monitoring. The means through which a GNSS-based integrity monitoring system can be programmed to detect mooring failures is presented and compared with alternative monitoring techniques. The potential for such a GNSS-based system to complement or replace other methods such as in-line load cell is discussed, particularly in scenarios where the installation or replacement of such direct mooring line monitoring technologies can be prohibitive from an operational or cost perspective.
The practical considerations gathered from seven combined years of operational mooring integrity on various offshore facilities are also discussed, including the use of metocean monitoring and forecasts, data rate, resolution, accuracy, delay and quality considerations, and the challenges involved in rolling out such a system on an operating facility.
This paper provides operators of floating facilities with insights into the kind of data which should be considered for an effective monitoring solution, as well as an approach to combine different data streams (vessel offsets, first order motions and environmental data) to derive a holistic assessment of mooring integrity. This information can be valuable for operators defining the scope of monitoring for new facilities or looking to get the most value out of the data on their existing facilities, where a system such as the one presented can provide a quantitative basis for operational decisions and improve the regulatory and risk compliance of offshore facilities.
Characterization of a New Green Material for Offshore Well Completions and Downhole Treatments
Authors: Monreal Gabriel , Zamora Frank , Ovrebo Hans Henrik , Orizondo Peter , Soidinsalo Otto 
Published: April 2022

Abstract
In oil and gas well construction during the drillouts or wellbore cleaning process, one of the most critical functions of land and offshore completions fluids is the ability to suspend solids effectively under extreme downhole conditions. Conventional agents such as xanthan gum, HEC and numerous other polymers have historically been used to accomplish this function, albeit with limitations. Functionally, these commonly used polymers depend primarily upon viscosity rather than elastic characteristics to suspend solids and require intensive chemical processing that leads to high deployment cost.
Recently microfibrillated cellulose (MFC) has been investigated as a prospective suspending agent in carrier fluids for extreme downhole conditions. MFC is a unique type of superfine cellulose fibrils obtained from fully sustainable sources that have been subjected to proprietary treatment procedures, resulting in fibril bundles consisting of lateral dimensions in the sub-micron scale and lengths up to micron scale with abundant terminal hydroxyl functional groups. When dispersed into aqueous solutions, the resulting fluid has been characterized to have several favorable rheological, chemical and mechanical properties.
Rheological measurements show the viscoelasticity of MFC dispersions is dominated by their storage modulus (G′ &gt; G″) even with fluids formulated with as low as 0.25 wt% (about 20 lbm MFC /1000 gallons). The result is a suspension that exhibits superior particle suspension properties compared on a mass basis to conventional materials such as guar, CMC, HEC and xanthan gum. In addition, MFC solutions exhibit comparatively high viscosities at low shear rates but thin by several orders of magnitude at high shear, a characteristic that implies less work on surface equipment while having the ability to suspend solids at very low pump rates. MFC dispersions also have an excellent brine tolerance, demonstrating stable suspensions over weeks in fluids containing up to 150,000 mg/L TDS. The dispersions are stable at downhole relevant temperatures, applicable at low and high pH levels and resistant to shear degradation. Finally, MFC originates from natural resources and is environmentally benign and biodegradable.
This paper describes the comprehensive characterization of the rheological and suspension properties that distinguish MFC from other conventionally used materials and make it fit-for-purpose as a robust, environmentally benign and high-performance suspending agent for downhole applications.
Anticipated Verification: A Simple Method for Representing Confidence and Trust in Metocean Forecast Information to Effectively Enhance Operational Decision-Making
Authors: Steele Edward , Mylne Kenneth , Neal Robert , Standen Jessica , Geertsema Gertie , Krikken Folmer , de Vries Hans , Amies Jessica , Brown Hannah , Upton Jon 
Published: April 2022

Abstract
It is easy to look back at a past forecast and assess how well it performed relative to the observed weather, but it is an obvious fact that such verification is only possible after the event has passed. Despite advances in skill in predicting variables such as wind speed or significant wave height, a perennial problem for offshore industry users interested in the incisive application of metocean forecast information to effectively enhance their operational decision-making is knowing how much ‘trust’ to place in these data at lead times beyond a few days ahead. Here, for the first time, we propose a method for the stratification of (past) verification statistics that applies unused information from the (future) forecast to anticipate how well the numerical weather prediction will perform a priori, providing a ‘weather-aware’ estimate of its skill (confidence) before the event has occurred.
Stratification is a method for being able to identify differences in characteristics of a particular subset of data, rather than just considering the whole population together. Since forecast performance is partly determined by the ‘drivers of predictability’ at that particular forecast horizon (e.g. prevailing atmosphere/ocean regime), we apply stratification on the basis of the classification of the large-scale weather present at the time to ‘filter’ past verification statistics, as considered in terms of the continuous ranked probability skill score. When used in real-time with a future forecast, the technique offers a refined estimate of the skill by taking advantage of the context of the weather being predicted. The translation of this ‘anticipated verification’ information into a qualitative description offers a robust indicator of confidence, suitable for (dynamically) guiding user trust in the forecast.
Using 2.75 years of archived forecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) extended-range (monthly lead time) ensemble prediction system (EPS) for an example location in the North Sea, the derivation of a dynamic (conditionally sampled) ‘trust index’ is presented; the application of which being evaluated using trials on unseen data from the same site. The benefit of the approach demonstrates significant potential for improving confidence in decision-making from individual short- to medium- range forecasts, as well as building trust in longer-range forecasts. While not a substitute for meteorologist guidance regarding the comprehensiveness of the various drivers of predictability at sub-seasonal timescales, this is none-the-less an important step in making such forecasts usable, useful and used.
Carbon Transportation and Storage, from Shore to Seabed
Authors: Mossop John , McLaughlin Daniel 
Published: April 2022

Abstract
The Energy Transition is redefining how energy is produced, consumed, and viewed. Many consumers today are requiring a more sustainable method of energy generation as a means of combatting climate change, and nations have pledged to take steps to control climate emissions. Newly developed methods of energy generation and solutions to decarbonize industrial zones, are essential to achieving carbon reduction commitments, and maintain the quality of life expected in today's world. The company has committed to continue to develop and improve its Green by Design™ products to further reduce customer CO2 emissions.
Investing the time and resources to develop equipment used in the injection and storage of CO2 in depleted subsea reservoirs, is a natural fit for most companies with subsea expertise. Having a cost-effective, safe and sustainable plan to transport, inject, store, and monitor CO2 has become the latest challenge facing the offshore industry, but as carbon taxes are implemented around the globe, the economic benefits outweigh the costs. Industry needs to find solutions to fulfill the worlds desire to inject and store CO2.
The development of partnerships/collaborations with logistics and storage development companies to create a plan to address the loading, transportation, injection, storage, and remote monitoring of CO2 is fundamental to enhancing the scale needed to address a global problem. This ‘shore to seabed’ solution allows industrial CO2 producers to cost-effectively reduce their carbon footprint. This coupled with field-proven wellhead and compact tree systems along with patented, carbon footprint reducing technology, can allow CO2 storage operators to economically store CO2 offshore long-term. Digital monitoring around the injection site, which is enabled by a move towards a full electric field further enables optimization and proactive maintenance.
Impact of Admixtures on Pumpability and Short-Term Mechanical Properties of Rock-Based Geopolymer Designed for Zonal Isolation and Well Abandonment
Authors: Chamssine Fawzi , Gargari Pedram , Khalifeh Mahmoud 
Published: April 2022

Abstract
Ordinary Portland Cement (OPC) is the primary binder material used for primary cementing and plug and abandonment (P&A). This material has many advantages in parallel with many shortcomings. However, OPC consumes a large amount of raw material and energy for the calcination of limestone emit greenhouse gasses. Geopolymers are inorganic polymers and have binding properties with a network structure of aluminum and silicate. Studies have shown that geopolymer is a one alternative for OPC having superior properties. However, the development of different chemical admixtures can contribute to the technology's development and fasten the prospects of application in the oil & gas industry. Impact of chemical admixtures sucrose, zinc (Zn), potassium (K) and alumina oxide (Al2O3) was studied. The properties analyzed include workability, setting time, fluid loss, viscosity, and uniaxial compressive strength. The use of sucrose, Zn species, K species, and Al2O3 was inspired from results presented in previous studies. The slurries were tested under a bottomhole circulating temperature 50° and a static temperature and pressure of 70° and 14 MPa respectively. The results indicate a retarding effect using sucrose and its combination with Zn and K species. On the other hand, Al2O3 acted as an accelerator on both workability and setting time. No significant effect was observed on fluid loss and viscosity profiles in comparison with the neat mix design. Poisoning effects from sucrose and Zn species were observed as well in the early compressive strength of cured samples after 1 day.
Mechanistic Modeling of Foam-Assisted EOR Simulations: Comparing Two Key Foam Generation Mechanisms
Authors: Izadi Mohammad , Cepeda-Salgado Betty , Kam Seung I. 
Published: April 2022

Abstract
Surfactant/foam enhanced-oil-recovery (EOR) processes have long been regarded as a practical means of overcoming subsurface heterogeneity and gravity segregation, leading to higher sweep efficiency and more cumulative oil recovery. In order to understand the outcome of such processes, modeling and simulating how foam propagates and behaves with the help of mechanistic foam models is crucial. There are largely two different versions of mechanistic foam models available in the literature, but no studies have been conducted to compare and contrast the two models within the context of the numerical calculation itself.
To clarify, the two versions of foam models are based on different types of pore-scale lamella-creation events, both taking advantage of bubble population balance simulations. The first is established on the mobilization-and-division mechanism, implementing the mobilization pressure gradient (∇Po) in the model to produce a large population of bubbles in porous media. On the other hand, the second is formed on the Roof-type snap-off mechanism, incorporating convection-dependent bubble creation at the germination sites into the model.
Therefore, this study investigates similarities and differences of the two different foam models and understand the pros and cons of each foam model. In line with that, this study is performed with the following tasks: (i) identify and borrow representative mechanistic foam models from the literature, (ii) make a fit to coreflood experimental data and determine foam model parameters for each model, and (iii) find the characteristics of the two models and their implications. The laboratory data for supercritical CO2 foam from Yin (2007) and Liu et al. (2010) is chosen to match (i) the pressure responses of no-foam/weak-foam state and strong-foam state and (ii) the steady-state pressure gradients (or drops) of strong foam state at various gas and liquid velocities. The models from Kovscek (1994) and Afsharpoor et al. (2010) are applied to perform the tasks. The results show that both models can reproduce experimental data equally nicely, but depending on how field operations are performed (rate-controlled vs. pressure-controlled) some additional sophisticated steps might be needed to make them equivalent in EOR simulations.
Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods
Authors: Srivastava Priyank , Bandyopadhyay Mainak , Chakraborty Shantanu , Patwardhan Samarth , Tran Huy 
Published: April 2022

Abstract
This paper presents a novel technique for planning and execution of Wireline Formation Testing (WFT) jobs using recent applications of machine learning. WFT measurements provide a link between the static petrophysical measurements and dynamic rock-fluid properties for enhanced formation evaluation. However, despite the availability of newer generation tools for these services, there are still obstacles related to formation test job design and real-time optimization. Skilled geoscientists traditionally use quicklook analysis and theoretical models in designing WFT jobs to acquire quality dataset. In practice, operators have access to a detailed data repository. However, the learnings from historical jobs may be overlooked during dynamic real time operations and engineers may typically regress to simpler or experienced based models. Some of the methods are self-made to reduce time and energy in data acquisition. Digitization and automation of data provide an opportunity for application of data analytics to support these real-time measurements for an objective evaluation of the dynamic data.
In this paper we propose use of unsupervised machine learning models, by which routinely recorded real-time measurements can be used to optimize the test design and real-time operations. Basic principles of interpretation of WFT are used to develop quality parameters and classification based unsupervised machine learning models (Self organized maps, K-means and Hierarchical Clustering). These machine based recommendations can aid in designing and optimizing the real-time job while ensuring quality of the results. In this way, highest quality testing results can be acquired to improve the integration of the dynamic data into the petrophysical analysis. This also will enable standards to be established for real-time data acquisition that can save testing time while improving data and quality.
Results show that machine learning models can be a powerful tool to develop a machine based recommendation systems for classification of WFT responses that are dependent on petrophysical parameters.