bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring and Optimizing Mud Motor Efficiency and Degradation in Real Time
Authors: Zhang Z. , Shen Y. , Chen W. , Ba S. , Scarioni F. , Vasquez D. , Belov D. , Converset J. 
Published: February 2024

Abstract
Mud motors are widely used in drilling operations to boost up drilling energy input and increase rate of penetration. It is essential to monitor the efficiency of the mud motor during drilling to ensure it being operated at optimum status. Besides, it is critical to identify the level of mud motor degradation and the trend in real time so that timely pull out of hole advise can be made before developing into a severe failure. This paper presents a real time solution to optimize mud motor efficiency and monitor its degradation based on streamed surface and downhole data, derived mud motor characteristics from modeling, and extracted insights from data analytics.
To compute mud motor efficiency and degradation in real time, it leverages surface operation parameters and average downhole rotational speed. By incorporating mud motor characteristic curve, we compute the mud motor torque, RPM, power input and output during operation. Then the real time efficiency and degradation of the mud motor is estimated. In the meantime, data analytics methods are exploited to identify the trend of the mud motor degradation and detect its critical change point, which is used to provide insights of the operation. In addition, a database has been established by applying the same computation on thousands of fields runs, which covers various hole sizes, bit types, mud motor models, locations, formations. The thresholds of mud motor efficiency and degradation were extracted statistically from the failed motor runs. In the real time application, those thresholds are adapted to evaluate risk levels based on the real time mud motor efficiency and degradation level.
The solution helps improve drilling performance by comparing the real time mud motor efficiency with selected offset runs with similar applications. Based on offset mud motor failure cases, the solution provides quantified real time degradation risk index which provides opportunities to identify signs of mud motor degradations and to intervene in early stage to prevent it from developing into catastrophic event. The solution has been deployed to the field locations and serves as part of the mud motor interpretation answer product in real time monitoring standard workflow.
Monitoring the efficiency and degradation of a mud motor is a not an easy task. In most cases, when the rubber chunks are found on the shaker, the mud motor is already severely degraded, and the performance has been compromised for long time. This paper presents a unique solution to monitor mud motor efficiency and degradation in real time and aims to capture the issue in early stage. It combines domain knowledge, data analytics, offset well analysis to derive real time solution to manage mud motor efficiency and degradation.
Near-Bit, Real-Time Measurements from an Innovative Ultra-Deep Electromagnetic Tool for Proactive Geosteering in Horizontal Wells
Authors: Wu H. , Clegg N. , Duriez A. , Ma J. , Wu D. , Bikchandaev E. , Lozinsky C. , Bittar M. 
Published: February 2024

Abstract
In some reservoirs, where optimum well placement requires that a well be placed close to a formation or fluid boundary, changes in the position of the boundary need to be identified early to allow prompt correction of the well path, preventing the well from intersecting the boundary and incurring a costly side-track. Even if the angle between the boundary and the well is only a few degrees, the distance between them can change rapidly. If a tool measure point is far from the bit, a reservoir exit may occur before corrective action can be taken.
An innovative, ultra-deep electromagnetic (EM) design, integrated with the rotary steerable system, places a sensor location 9 ft behind the bit. The novel system enables near-bit, ultra-deep azimuthal resistivity (UDAR) measurements, providing inversion results for the surrounding structure much earlier than existing ultra-deep EM systems. The new UDAR tool achieves an inversion detection range of over 100 ft, with the sensor measure point very close to the bit. This allows for earlier identification of changes in formation or fluid dip, facilitating proactive well-placement steering decisions.
This paper investigates how the depth of detection of formation boundaries, the relative angle between the boundary and the wellbore, and the sensor measure point affect the ability to avoid hazards during well placement operations. There are common limits on the maximum allowable dog-leg severity (DLS) due to the need for later passage of the completion assembly through the wellbore. Early identification of any change in the dip of a boundary is therefore critical. The closer the sensor measure point is to the bit, the quicker this trend can be identified and the required remedial action taken, within the prevailing DLS constraints.
In summary, the real-time UDAR inversion of the new system enables much earlier geosteering decisions than existing technologies, resulting in a significant advance in well-placement technology. Reducing or eliminating geosteering risks helps reduce overall well time, maximize the value of each well, and improve the overall efficiency of a field development.
Achieving Operational Excellence Using an Interactive Drilling Time Analysis and Benchmarking Methods
Authors: Amer Mustafa , Radhwi Asem , Othman Amr 
Published: February 2024

Abstract

OBJECTIVE / SCOPE
Performance improvement to reduce the cost is a common goal for the industry, there are many initiatives and processes now in place, applying different techniques. Most of these techniques are dependent on operational time-distribution analysis, to benchmark current performance and predict future performance, while identifying improvement opportunities. An objective, standard, yet flexible tool is required to support these different approaches. Authors will share their experience of developing a method and tool that serves this purpose.

METHODS, PROCEDURES, PROCESS
The tool provides an environment that enables users perform different types of analytics: descriptive, predictive and prescriptive on the operational time-distribution data. The data sources in this environment are integrated seamlessly to facilitate comparative analyses on many aspects of drilling performance providing the following: Compare actual performance with the planAllow pre-, post-, and active-execution performance analysisIdentify current performance limitations and potential improvementBenchmark performance for different teams, contractors and service providersInvestigate lost time incidents; evaluate their impact; and discover root causes behind itProduce an automated Ultimate Drilling Curve and build realistic time estimates for future wells

RESULTS, OBSERVATIONS, CONCLUSIONS
The proposed system architecture contains four main modules, which provide the aforementioned functionalities, through comprehensive interactive tools to facilitate analysis of drilling time at different levels of granularity. These modules are: Wells Similarity Identification, filtration, and Selection moduleGeneral Time Distribution Comparison moduleProgress Charts (time versus depth) Analysis moduleLearning Curve Evaluation module
The proposed system functionality will be introduced through this paper in addition to a methodology to utilize these tools to perform variety of comprehensive performance analysis studies.

NOVEL/ADDITIVE INFORMATION
The proposed system is an integrated interactive platform for different user groups to run sophisticated performance analytics and studies efficiently and effectively without being limited to specific scenarios or data types.
The Benefit of In-Bit Sensing for Efficient Drilling of Deep Reach Lateral Wells in HFTO Prone Interbedded Abrasive Lithologies in North America Land
Authors: Hanafy A. , Kueck A. , Pauli A. , Huang X. , Bomidi J. 
Published: February 2024

AbstractReaching the pay zone of deep lateral wells requires several bits due to vibration-prone interbedded abrasive lithologies in North America Land. Excessive wear of cutters combined with extreme torque fluctuations results in reduced ROP and premature failures of BHA tools. Early POOH leads to 24 h to 48 h of non-productive time due to BHA tripping. This puts enormous economic pressure on these challenging pay zones. This paper presents a detailed analysis of downhole and in-bit sensing data to increase the operational stability window enabling enhanced bit durability and smoother drilling conditions for economic drilling of long lateral wells.The proposed bit's operation optimization framework is based on a detailed analysis of in-bit high-frequency gyroscope and accelerometer data correlated to vibration and Gamma-ray measurements of MWD and LWD tools. The data analysis reveals High-Frequency Torsional Oscillation (HFTO) prone formations and HFTO frequencies, amplitudes and durations while drilling in lithologies with different rock strengths. The results are compared to lab tests drilling on a full-scale drilling rig using representative rock cores under high confining pressure. The lab measurements are used to model the performance of drill bits accurately and develop best practices for operations. An operational stability map and a drilling guideline have been built and communicated with the region to enable longer reach wells and stable drilling.The data analysis shows a correlation between the lithological shaleness factor and the BHA sensitivity to excite HFTO among other vibrations. The higher the shale content of the gamma ray track the lower the probability of triggering HFTO. These findings are reflected in the lab while drilling interbedded cores at high confining pressure. The data are used to calibrate bit models for design optimization. The application of drilling guidelines on operating 9 bits resulted in a total savings of $500K per well and reduced the non-productive tripping time by 4 days. Guided drilling practices increased the drilled footage per bit by 50%, and reached the same dull state as previous operations. Control of HFTO and vibrations reduced early failures of BHA tools enabling longer intervals and less unnecessary trips.The lack of wire line logging in extended reach lateral wells limits the understanding of targeted formations. This framework enables deeper formation insights that unlock the optimization of performant drill bits. The adoption of this practice promises longer footage per bit, less downhole vibrational issues, and efficient completion of interbedded abrasive lithologies in the future.
Deep Learning Real-Time Bit-Wear Model Approves to be Robust and Transferable in Hard Drilling Applications
Authors: Zhan Guodong David , Aljohar Abdulwahab , Qahtani Yazeed , Huang Xu , Luu Trieu Phat , Furlong Ted , Bomidi John 
Published: February 2024

Abstract
In a previous study, we reported the development of a bit-agnostic AI deep learning wear model and showed the successful real-time deployments in multiple field bit runs. To test AI/DL model robustness and transformability, we extended the AI/DL model to another similar hard drilling application with different hole sizes, BHAs, bit designs, and lithology. The model robustness is successfully tested using a smaller dataset of subsurface logging and surface drilling data.
AI/DL bit wear model employs an unsupervised bi-directional Long short-term Memory-based Variational Autoencoder (biLSTM-VAE) to project raw drilling and formation logging data into a lower-dimensional latent space. XGboost classifier is used to predict real-time bit-wear in the pre-trained latent space. Physics-based input feature engineering is conducted to extract meaningful information from the raw data. To test the model robustness when transferring to a different drilling application, only Gamma logging data and a smaller offset training dataset is applied. The deep neural network was trained in an unsupervised manner and the bit-wear estimation is an end-to-end process.
AI/DL wear model was trained on twenty offset bit runs. To test the model robustness, different hole sizes and BHAs were selected in addition to bit design and lithology changes. Unsupervised training results show biLSTM-VAE model successfully extracted bit wear features and clustered bit states in the latent space. Bit wear estimation using XGboost matches with the field results for the test dataset. In the previous real-time study, Gamma ray, density and porosity loggings were used in AI/DL model for downhole formation interpretation. The model robustness test in this study demonstrates the AI/DL model can be applied to applications with only Gamma data. BHA feature testing suggests separate AI/DL models need to be trained for RSS and motorized RSS systems.
AI model robustness and transformability is crucial for the success of AI model field deployments. The model robustness test in this study shows encoding drilling data into a lower dimensional space through unsupervised learnings could improve the model robustness and overcome offset training data quality issue. AI/DL results suggest exploratory data analysis of input drilling features such as BHAs and formation loggings is important for the model accuracy.
A Step Change; Achieving Excellence in Optimizations and Exceeding Global Top Quartile in a Brown Field
Authors: Al-Hajri A. , Al Shukaili G. T. , Urdaneta G. , Al Adi F. , Kaabi M. , Al-Kindi S. S. , Kiyumi W. 
Published: February 2024

AbstractThis SPE paper provides valuable insights into a brown field facing significant challenges, including a sharp decline rate resulting from nearly exhausted optimization opportunities, a rapid increase in water cut, and high deferment due to frequent pump failures. However, through a well-coordinated and multidisciplinary approach, the field managed to achieve a remarkable 15% optimization gain, surpassing the global top quartile, and improved the decline rate from 11% in 2020 to -7.7% in 2022.The success of the field's transformation was driven by an integrated field review, which uncovered overlooked opportunities through extensive participation from various disciplines. Key to this success was the conversion project, which involved changing Beam pump (BP) or Progressing Cavity Pump (PCP) to Electrical Submersible Pump (ESP) and BP to Long Stroke Pumping Unit (LSU), significantly boosting production and enabling the reopening of Closed-In Wells (CIWs) that greatly benefited from these conversions. Furthermore, the implementation of other identified opportunities such as Water Shut-off (WSO), stimulation, Proactive Pump Replacement (PPR), and pump upsize resulted in the execution of a total of 82 activities in 2021 and 2022, all accomplished without encountering H2S issues.The maturation of these activities yielded impressive results in terms of decline rate improvement, optimization gain, and deferment reduction. The field's decline rate notably improved from around 11% in 2020 to -7.7% in 2022. This achievement can be attributed to the substantial increase in optimization gains from 3% to 15.3%, and a significant decrease of 68% in unscheduled subsurface deferment. The conversion project, particularly the shift to ESP, significantly increased net oil production by enabling higher gross production, contributing to the remarkable optimization gain. The Long-Stroke Pumping Unit conversion also demonstrated a remarkable 40% energy saving.The implementation of the conversion project also positively impacted deferment by significantly reducing pump failures, as evident from the drop in the failure index from 30% in 2020 to 18.8% in 2022. Despite the overall improvements in various aspects, it is essential to acknowledge that water production increased due to the higher gross production resulting from the ESP conversions.The paper highlights the significance of this brown field's unprecedented achievement, surpassing the global top quartile in optimizations despite almost depleted opportunities. It showcases how thinking outside the box and employing engineering judgment can lead to a remarkable transformation in a matured field. The success story demonstrates how the spark of innovation evolved into a flame that far exceeded global top quartile benchmarks, making this field one of the most important and noteworthy in the cluster.
Prevention of Stuck Pipe Events and Robust Real-Time Identification of Root Cause Using Physics Based Models in Combination with Bayesian Network Models
Authors: Yi Michael , Ashok Pradeepkumar , Behounek Michael , White Spencer , Peroyea Trey , Thetford Taylor , Hickin Gary , Pearce Julie 
Published: February 2024

Abstract
When stuck pipe incidents happen, they can drastically increase the cost of a well. Although much progress has been made in stuck pipe prevention it has not been eliminated. Therefore, there is an ever increasing need to be able to automatically estimate the potential for a stuck pipe event so that preventative measures can be taken. Additionally, when the pipe does get stuck, if the reason can be determined quickly, non-productive time (NPT) can be substantially reduced. The objective here is to combine physics-based models with Bayesian networks to better diagnose drilling events and prevent stuck pipe NPT.
The approach focuses on identifying three main symptoms: pack off, overpull and off-bottom rotational drag. These are detected through three separate ML models, which in this case are primarily Bayesian belief networks. Inputs to these ML models require the use of a real-time physics-based torque and drag model as well as a real-time hydraulics model. These models require contextual data which is automatically routed to the rig platform. The features used in the ML models were arrived at after a careful detailed study of a large dataset of previous stuck pipe incidents.
The dataset used for building the models were from wells in North American and Middle East land operations, and North Sea offshore operations. This helped build a generalized model which is expected to be applicable irrespective of the geographic location of the well. The contextual data that is required consists of the survey data, the bottom hole assembly information, mud properties, and casing information. While it is uncommon for such data to be generally available at the rig site for real-time analysis, the team deployed novel automated data transfer mechanisms to enable real-time data transfer in all three operation locations. Tracking the data continuously also provided a means to determine when a stuck pipe incident occurred in real-time, and using data up until the current time, the algorithm probabilistically estimated the cause. This can allow for quick remedial action, which in turn leads to reduced NPT. The approach has now been deployed in the field after validations on historical datasets.
This is the first time a combination ML and physics-based approach to stuck pipe detection has been deployed at a rig site. The models are not black boxes and provide prescriptive recommendations on measures to take to avoid stuck pipes. If a pipe does get stuck, the user is provided with immediate notification of likely root cause. The paper provides details on the models used and is expected to significantly improve stuck pipe prevention in the industry.
Research and Application of Completion and Workover Fluid Technology in Heavy Oil Reservoir in Ecuador
Authors: Baojun Li , Pingya Luo , Xiangyang Zhao , Long Chen 
Published: February 2024

Abstract
This paper aims at the problems such as emulsification and thickening of crude oil mixed with working fluid, organic matter precipitation, crystallization and precipitation of high density completion fluid under formation conditions, and solid damage of oil formation caused by working fluid in Ecuador completion construction. Through the physical property analysis of formation fluids in Ecuador, as well as the mechanism analysis of emulsification and precipitation, high efficiency mutual solvent, reverse emulsifier and no solid phase aggravating agent are studied in the laboratory, and the solid phase free, high density, low damage and low corrosion completion and workover liquid system and process technology suitable for Ecuador's heavy oil reservoir are formed, and the evaluation and performance optimization are carried out. The density of the working fluid is 1.10-1.55g/cm3, the pH value is 6-8, the API filtration loss is 10.5mL/30min, the core penetration damage rate is 8.7%, the clay anti-swelling rate is 97.9%, and the corrosion rate is 0.039mm/a. Scale inhibition capacity (turbidity value after mixing with 5000mL/L NaHCO3 solution) 6.98NTU. According to different reservoir flow properties and construction conditions, compatibility control technology is studied, completion fluid formulations in different blocks and working conditions are tested, and field tests are carried out.10 field tests are completed in Parahuacu, Tambococha and other blocks, and the construction work is smooth and good results are achieved.
The Application of Foam and Gel Compositions to Control Gas Inflow in Production Wells: From Laboratory Studies to Injection
Authors: Belovus P. N. , Penigin A. V. , Shirobokov A. V. , Oleynik A. A. , Minnebaev B. R. , Saifullin E. R. , Varfolomeev M. A. 
Published: February 2024

Abstract
The formation of gas cones and the formation of local breakthroughs is a serious complication in the development of thin oil rims with gas caps. It leads to losses in oil production due to the failure of pumping equipment or choking of a productive interval located behind the gas breakthrough interval. With intensive gas breakthrough, the feasible well life is significantly reduced and, as a result, oil recovery factor is hindered.
To plan gas inflow reduction measures, it is necessary to conduct special laboratory studies to remove the main uncertainties that affect the success and efficiency. These include physical characteristics of the injection agent under operating conditions, its ability to block the unwanted phase, the selectivity of the treatment, the required injection gradients, and agent shear gradients.
Depending on the characteristics of the reservoir and formation fluids, various types of chemical compositions are used to alleviate or prevent gas breakthrough: foams, foam polymers, foam gels, gels, microcements.