bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation
Authors: Magana-Mora Arturo , AlJubran Mohammad , Ramasamy Jothibasu , AlBassam Mohammed , Gooneratne Chinthaka , Gonzalez Miguel , Thiel Tim , Deffenbaugh Max 
Published: December 2021

Abstract
Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs.
Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events.
Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT.
Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.
Machine Learning Microservice for Identification of Accident Predecessors
Authors: Gurina Ekaterina , Antipova Ksenia , Klyuchnikov Nikita , Koroteev Dmitry 
Published: December 2021

Abstract
Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar.
The model for forecasting the drilling accidents is based on the "Bag-of-features" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model.
The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model.
The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.
The Value of Seismic Data in Conventional and Unconventional Fields
Authors: Alghamdi Nawaf , Alghenaim Hamad 
Published: December 2021

Abstract
The paper illustrates the value of seismic data in different environments after assessing the benefits and costs of processes such as seismic acquisition, seismic processing and seismic interpretation. Global examples from conventional and unconventional fields are discussed to show how seismic data plays a significant role in determining low-risk and high-reward wells and also eliminating the high-risk and low-reward wells.
This paper shows an example of a conventional field in the state of Kansas, USA, where the net present value (NPV) increased by more than 17 times when 3D seismic data was acquired, while in an unconventional field the commercial success rate rose from 30% to 70% due to 3D seismic acquisition. However, two offshore fields in the Republic of Trinidad and Tobago are discussed to show that the NPV as impacted by advanced seismic processing was more than 111 ($M). Another example comes from Viking Field, a conventional field in Canada, where the NPV was increased from 3800 ($M) to 5000 ($M) when the seismic data was re-processed. Furthermore, the value of investing in seismic data was investigated and quantified by comparing two synthetically modeled scenarios in Saudi Arabia.
Overall, the four examples from North America, Central America and Saudi Arabia illustrate that investment in seismic data has a positive impact on both conventional and unconventional fields. That provides strong evidence to encourage more investments in geophysical technologies.
A Non-Damaging Gelled Acid System Based on Surface Modified Nanoparticles
Authors: Kalgaonkar Rajendra , Bataweel Mohammed , Alkhowaildi Mustafa , Sahu Qasim 
Published: December 2021

Abstract
Gelled acid systems based upon gelation of hydrochloric acid (HCl) are used widely in acid stimulation treatments to prevent fluid leak-off into the high permeable zones of a reservoir. The gelled-up fluid system helps retard the acid reaction to allow deeper acid penetration for hydrocarbon productivity enhancement. Conventional in-situ crosslinked gelled acid systems are made up of polyacrylamide gelling agent, iron-based crosslinker, and a breaker chemical in addition to other additives, with the acid as the base fluid. The polymer-based systems can lead to damage to formation due to a variety of reasons including unbroken polymer residue. Additionally, the iron-based crosslinker systems can lead to scaling or precipitation after the acid reacts with the formation, resulting in formation damage and lowering of hydrocarbon productivity.
In this paper, we showcase a new nanoparticles-based gelled acid system that does not contain any polymer or iron-based crosslinker that can potentially damage the formation. It comprises nanoparticles, a gelation activator, acidizing treatment additives along with HCl. The new in-situ gelled acid system has low viscosity at surface making it easy to pump. With increase in the temperature and as the acid spends there is a viscosity increase. The viscosification and eventual gelation of the new system can be achieved as the acid reacts with a carbonate formation. As the acid further reacts and continues to spend, the gel demonstrates reduction of viscosity. This assists in a better cleanup post the acidizing treatment.
Various experimental techniques were used to highlight the development of the nanoparticle-based acid diversion fluid. The gelation properties of the acid system, as a function of acid strength and temperature, are investigated. Static and dynamic gelation studies as a function of time, temperature and pH are reported. It is demonstrated that the viscosification property is a function of pH and the gelation occurs in a pH widow from 1 to 5 pH units. The gelation performance of the new system is evaluated at temperatures up to 300°F. The effect of different types of surface modification chemistries on the gelation properties is investigated. It is also shown that the gelation and viscosity reduction is entirely a pH dependent phenomenon and does not require any additional breaker chemistry; and therefore provides more control over the system performance.
The new gelled acid system overcomes the inherent challenges faced by conventional in-situ crosslinked gelled acid systems; as it is based upon nanoparticles making it less prone to formation damage as compared to a crosslinked polymer-based system.
Advanced Horizontal Well Correlation Method for Dynamic Update of Subsurface Layers While Geosteering
Authors: Al-Maskeen Abdul Mohsen , Ali Sadaqat 
Published: December 2021

Abstract
A new automated approach to well correlation is presented that utilizes real-time Logging While-Drilling (LWD) data and predicted well curve to dynamically update subsurface layers during geosteering operations. The automatically created predicted log and a dynamically updated structural framework provides the foundation of the process. The predicted log is created using vertical sections of the nearby wells, which provide high confidence for determining depth and stratigraphic position of the geosteered well. The results give a better understanding of thickness variation in the horizontal part of the reservoir and maximize the reservoir contact (Sung, 2008).
A new advanced methodology introduced in this study involves the creation of a dynamic structural framework model, from which horizontal well correlation is performed using real-time well logs and predicted logs that are generated from adjacent wells. The predicted logs are correlated to the LWD logs using anchor points and an interactive stretching and squeezing process that honors true stratigraphic thickness. Each new anchor point results in the creation of an additional control point that is used to build a more precise structural framework model.
This new approach enables more rapid well log interpretation, increased accuracy and the ability to dynamically update the subsurface model during drilling. It also enables more efficient steering of the wellbore into the most productive zones of the reservoir. This study demonstrates how wells with over 10,000 feet of horizontal reservoir contact can be correlated in a real-time geosteering environment in a dynamic, efficient and accurate manner. The proposed process dramatically helps reduce the cost of drilling and the time it takes to dynamically regenerate accurate updated maps of the subsurface. It represents a major improvement in the understanding and modeling of complex, heterogeneous reservoirs by fostering a multi-disciplinary environment of cross-domain experts that are able to collaborate seamlessly as asset-teams. Both accuracy and efficiency gains have been realized by incorporating this methodology in the characterization of multi-stacked reservoirs.
Nanoemulsions: A Versatile Technology for Oil and Gas Applications
Authors: AlJabri Nouf , Shi Nan 
Published: December 2021

Abstract
Nanoemulsions (NEs) are kinetically stable emulsions with droplet size on the order of 100 nm. Many unique properties of NEs, such as stability and rheology, have attracted considerable attention in the oil industry. Here, we review applications and studies of NEs for major upstream operations, highlighting useful properties of NEs, synthesis to render these properties, and techniques to characterize them. We identify specific challenges associated with large-scale applications of NEs and directions for future studies. We first summarize useful and unique properties of NEs, mostly arising from the small droplet size. Then, we compare different methods to prepare NEs based on the magnitude of input energy, i.e., low-energy and high-energy methods. In addition, we review techniques to characterize properties of NEs, such as droplet size, volume fraction of the dispersed phase, and viscosity. Furthermore, we discuss specific applications of NEs in four areas of upstream operations, i.e., enhanced oil recovery, drilling/completion, flow assurance, and stimulation. Finally, we identify challenges to economically tailor NEs with desired properties for large-scale upstream applications and propose possible solutions to some of these challenges. NEs are kinetically stable due to their small droplet size (submicron to 100 nm). Within this size range, the rate of major destabilizing mechanisms, such as coalescence, flocculation, and Ostwald ripening, is considerably slowed down. In addition, small droplet size yields large surface-to-volume ratio, optical transparency, high diffusivity, and controllable rheology. Similar to applications in other fields (food industry, pharmaceuticals, cosmetics, etc.), the oil and gas industry can also benefit from these useful properties of NEs. Proposed functions of NEs include delivering chemicals, conditioning wellbore/reservoir conditions, and improve chemical compatibility. Therefore, we envision NEs as a versatile technology that can be applied in a variety of upstream operations. Upstream operations often target a wide range of physical and chemical conditions and are operated at different time scales. More importantly, these operations typically consume a large amount of materials. These facts not only suggest efforts to rationally engineer properties of NEs in upstream applications, but also manifest the importance to economically optimize such efforts for large-scale operations. We summarize studies and applications of NEs in upstream operations in the oil and gas industry. We review useful properties of NEs that benefit upstream applications as well as techniques to synthesize and characterize NEs. More importantly, we identify challenges and opportunities in engineering NEs for large-scale operations in different upstream applications. This work not only focuses on scientific aspects of synthesizing NEs with desired properties but also emphasizes engineering and economic consideration that is important in the oil industry.
Innovative Approach of Drilling Risk Identification and Mitigation Using Drilling Automation Services: Case Studies
Authors: Roy Chowdhury Ashabikash , Forshaw Matthew , Atwal Narender , Gatzen Matthias , Habib Salman , Afolabi Jonathan 
Published: December 2021

Abstract
In the increasingly complex and cost sensitive drilling environment of today, data gathered using downhole and surface real-time sensor systems must work in unison with physics-based models to facilitate early indication of drilling hazards, allowing timely action and mitigation. Identification of opportunities for reduction of invisible lost time (ILT) is similarly critical. Many similar systems gather and analyze either surface or downhole data on a standalone basis but lack the integrated approach towards using the data in a holistic decision-making manner. These systems can either paint an incomplete picture of prevailing drilling conditions or fail to ensure system messages result in parameter changes at rigsite. This often results in a hit or miss approach in identification and mitigation of drilling problems.
The automated software system architecture is described, detailing the physics-based models which are deployed in real-time consuming surface and downhole sensor data and outputting continuous, operationally relevant simulation results. Measured data from either surface, for torque & drag, or downhole for ECD & ESD is then automatically compared both for deviation of actual-to-plan, and for infringement of boundary conditions such as formation pressure regime. The system is also equipped to model off-bottom induced pressures; swab & surge, and dynamically advise on safe, but optimum tripping velocities for the operation at hand. This has dual benefits; both the avoidance of costly NPT associated with swab & surge, as well as being able to visually highlight running speed ILT. All processing applications are coupled with highly intuitive user interfaces.
Three successful deployments all onshore in the Middle East are detailed. First a horizontal section where real-time model vs. actual automatic comparison of torque & drag samples, validated with PWD data allowed early identification of poor hole cleaning. Secondly, a vertical section where again the model vs. actual algorithmic automatically identified inadequate hole cleaning in a case where conventional human monitoring did not. Finally, a case is exhibited where real-time modelling of swab and surge, as well as intuitive visualization of the trip speeds within those boundary conditions led to a significant increase in average tripping speeds when compared to offset wells, reducing AFE for the operator. Common for all three deployments was an integrated well services approach, with a single service company providing the majority of services for well construction, as well as an overarching remote operations team who were primary users of the software solutions deployed.
Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence
Authors: Mezghani Mokhles , AlIbrahim Mustafa , Baddourah Majdi 
Published: December 2021

Abstract
Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model.
The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model.
The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time.
CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.
Condensate Banking Removal Using Slow Release of In-Situ Energized Fluid
Authors: Al-Nakhli Ayman , Hassan Amjed , Mahmoud Mohamed , Al-Baiz Abdualilah , Buhaezah Wajdi 
Published: December 2021

Abstract
Condensate banking represent a persistent challenge during gas production from tight reservoir. The accumulation of condensate around the wellbore can rapidly diminish gas production. When reservoir pressure drop below dew point, condensate start to dropout from gas phase, filling pores and permeable fractures, and block gas production. There are several strategies to mitigate condensate banking, however, these strategies are either demonstrate limited results or are economically not viable.
In this study, a novel method to mitigate condensate was developed using thermochemical reactants. Slow-release of thermochemical reactants inside different core samples was studied. The effect of in-situ generation of gas on the petrophysical properties of the rock was reported. Thermochemical treatment was applied to recover condensate on sandstone and carbonate, where the reported recoveries were around 70%. However, when shale sample was used, the recovery was only 43%.
Advanced Equation-of-State (EoS) compositional and unconventional simulator (GEM) from CMG (Computer Modelling Group) software was used to simulate thermochemical treatment and gas injection. The simulation study showed that thermochemical stimulation had increased production period from 3.5 to 22.7 months, compared to gas injection.