bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-Driven PHM Solution for Health Monitoring of Mud Motor Power Sections While Drilling
Authors: Belov Dmitry , BA Samba , Liu Ji Tang , Kolyshkin Anton , Rocchio Sergio Daniel 
Published: October 2021

Abstract
Mud motors are widely used for directional and performance drilling. Due to the extremely challenging operating conditions, they are prone to failures, resulting in unnecessary maintenance repair costs as well as unpredictable and very costly drilling failure.
Until now, the oil and gas industry has lacked reliable procedures to monitor and maintain the health of the mud motor power sections. Recently, we systematically addressed this problem with an industry unique prognostic health management solution, which not only tracks remaining useful life (RUL), but also creates a new failure prevention scheme for operators. The key objective of this solution is to reduce maintenance costs and improve mud motor fleet reliability. It's based on a high-fidelity model and uses a hybrid approach by combining a high-fidelity physics-based model of a power section and data-driven approaches with machine learning techniques for real-time applications.
The new methodology was tested in the field with great success. The verification of the created solution was completed based on numerous field data from Saudi Arabia and Argentina. Comparison of the predicted mud motor fatigue values with the actual observed post-job conditions and job failures demonstrated high fidelity of the developed models. The whole solution is currently being integrated into a drilling platform including the maintenance system, the well construction planning, and the execution. The first application of the workflow was deployed in the field in Colombia targeting reduction of maintenance cost and failure avoidance. The result was outstanding, with the initial deployment bringing about 27% of projected yearly maintenance savings and 10% of projected yearly failure reduction. It enables using the equipment to the full extent with increased drilling performance without sacrificing reliability. In addition, it optimizes the entire fleet management with reduced cost of logistics and maintenance.
The findings of this paper demonstrate the value of the mud motor PHM solution for the oil and gas industry by providing accurate prognosis of power section health, leading to reduced costs, minimized NPT, and increased operational reliability.
Geomechanically Driven Casing Deformation During Multistage Perf and Plug Fracturing Operations: Investigation and Mitigation
Authors: Gandomkar Arjang , Katz David , Gomez Ricardo , Gundersen Anders 
Published: October 2021

Abstract
Casing Deformation has presented itself in numerous unconventional basins. Severe deformation interferes with multistage fracturing, in particular with plug-and-perforation (also known as plug-and-perf) operations, the most common stage isolation method in unconventional development. Casing Deformation can greatly impact 20-30% of field productivity of horizontal wells in certain US shale and tight oil fields (Jacobs, 2020). Reservoir accessibility and well integrity are the two separate issues when considering casing deformation. In this paper, the impact of geomechanically driven casing deformation on reservoir accessibility that in turn affects production and economics, will be discussed.
Origin of casing deformation within a target zone lies in natural fractures placed in highly anisotropic stress regimes. When these fractures are perturbed by hydraulic stimulation, slow slip or dynamic failure of the rock may occur. This phenomenon is intensified by active tectonics, high anisotropic in-situ stresses, and poor completion practices, i.e., poor cement. This paper evaluates these processes by demonstrating failure conditions of wellbores in different stress states and well orientations representative of unconventional basins. It reviews how these conditions can be evaluated in the reservoir, so risk can be estimated. The mitigation procedures to reduce casing deformation impact to operations through either well planning or completions design are discussed. Finally, this paper will also review an alternative completion method to plug-and-perf that allows limited entry completion technique in restricted ID casing due to casing deformation with a field case study.
New LWD Image Improvement Method to Mitigate Interpretation Risks
Authors: Zhang Tianhua , Yang Shiduo , Shrivastava Chandramani , A Adrian , Bize-Forest Nadege 
Published: October 2021

Abstract
With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling).
At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images.
In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections.
Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.
Numerical Analysis of the Behavior of Gas Hydrate Layers After Cementing Operations
Authors: Merey Sukru , Eren Tuna , Polat Can 
Published: October 2021

Abstract
Since the 2000s, the number of gas hydrate wells (i.e., exploration wells, production test wells) has increased. Moreover, in the marine environment, gas hydrate zones are drilled in conventional hydrocarbon wells. Different than conventional hydrocarbon wells, the heat released with cement hydration cannot be ignored because gas hydrates are heat sensitive. In this study, by analyzing different cement compositions (conventional cement compositions and novel low-heat of hydration cement), it is aimed to investigate the effect of the heat of cement hydration on gas hydrate zones near the wellbore. For this purpose, numerical simulations with TOUGH+HYDRATE simulator were conducted in the conditions of the Nankai Trough gas hydrates. According to the numerical simulations in this study, if the increase in temperature in the cemented layer is above 30°C, significant gas hydrate dissociation occurs, and free gas evolved in the porous media. This might cause gas channeling and poor cement bond. The heat released with cement hydration generally affects the interval between the cemented layer and 0.25 m away from the cemented layer. Within a few days after cementing, pressure, temperature, gas hydrate saturation, and gas saturation returned to almost their original values.
Effects of Non-Newtonian Fluid Characteristics on Flow Dynamics in Polymer Flooding: a Lattice Boltzmann Study
Authors: Wei Bei , Hou Jian , Zhao Ermeng 
Published: October 2021

Abstract
The flow dynamics of non-Newtonian fluid in porous media is much different from the Newtonian fluid. In this work, we establish a lattice Boltzmann model for polymer flooding taking into both the power law fluid properties and viscoelastic fluid properties. Using this model, we investigate the viscosity distribution in porous media, the local apparent permeability in porous media, and the effect of elastic force on the remaining oil in dead ends.
Firstly, we build a single phase lattice Boltzmann model to evolve the fluid velocity field. Then the viscosity and shear rate in each lattice can be calculated based on the relaxation time and velocity field. We further make the fluid viscosity change with the shear rate according to the power-law fluid constitutive equation, consequently establish the lattice Boltzmann model for power law fluid. Moreover, we derive the Maxwell viscoelastic fluid model in integral form using Boltzmann superposition principle, and the elastic force is calculated from the divergence of the stress tensor. We then couple the elastic force into the lattice Boltzmann model by Newton's second law, and finally establish the lattice Boltzmann model of the viscoelastic fluid. Both the models are validated against analytical solutions.
The simulation results show that when the power-law index is smaller than 1, the fluid viscosity shows a distribution of that viscosity is higher in pore center and lower near the wall; while when the index is larger than 1, the fluid viscosity shows a opposite distribution. This is because the pore center has a high velocity but a low shear rate, while the boundary has a low velocity but a high shear rate. Moreover, the local apparent permeability decreases with the power law index, and the number of hyper-permeable bands also decreases. In addition, the local permeability shows pressure gradient dependence. Considering the viscoelasticity effects, the displacement fluid has a clear tendency to sweep deeply into the dead end, which improves the oil washing efficiency of the dead end.
The model provides a pore scale simulation tool for polymer flooding and help understand the flow mechanisms and enhanced oil recovery mechanisms during polymer flooding.
Sharpening a Cyclic Solvent Injection Technique in a Heavy Oil Reservoir from the Variation of the Nitrogen Injected as Dispersing: A Field Pilot, Colombia
Authors: Arias Andres Solano , Navarro Edgar Garzon , Munevar Fernando Contreras , Ortiz Isaac Luque 
Published: October 2021

Abstract
This paper analyzes the use of a cyclic solvent injection technique (CSI) as a non-thermal EOR alternative to cyclic steam stimulation (CSS) for increasing the heavy oil recovery in a shallow reservoir located at the middle Magdalena basin in Colombia.
A pilot well with less than 30% of water-cut and 10.9 °API was selected. Heavy natural gasoline of 30 °API obtained from the same reservoir was injected by using nitrogen (N2) as dispersing gas.
Three procedures were performed being the procedure A, a Solvent slug injection of 60 bbl through the annular pushed and dispersed by 46,444 m3 (1,640 Mscf) of N2 immiscible (considering the low reservoir pressure). The procedure B consisted of injecting the same Solvent volume, but this time pushed by a third part of the N2 injected previously 15,481 m3 (547 Mscf). The procedure C consisted of only inject the same N2 volume than B procedure to analyze the heavy oil response without Solvent. There were collected production data, °API (by hydrometer), gas-flow and gas-gravity values using a liquid level software. Knowing the °API of each component in the laboratory—Solvent and heavy oil—the Solvent concentration from the real °API produced in production stages was calculated. All procedures had 48 hours of soaking, followed by a flowing process to tank to carefully release the excess of N2 before starting the production stage, avoiding gas lock issues.
Without considering the Solvent injected, incremental oil production in procedure A was 232 bbl, in procedure B was 120 bbl and for procedure C, incremental oil only reached 11 bbl. With the last result it was determined the N2 injection by itself as a production mechanism without the Solvent effect in the in-situ heavy oil had a negligible effect on incremental oil. The gas-gravity showed the gas composition became heavier along the time, this considering the high-frequency N2 injections swept the methane near the well, requiring more time to produce the N2 traces from the porous media. The excess of N2 as a heavy Solvent dispersing mechanism does not warrant a better dilution effect since as observed in A and B procedures, Solvent concentration in the early production stage never dropped below 35% (17 °API), regardless of the N2 volume injected in the first two days.
Finally, although A procedure had more incremental oil production (+93% than B), less N2 injected in B procedure was more efficient (+55% than A) regarding the incremental oil and N2 injected ratio (ONR).
Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence
Authors: Al-Sabaa Ahmed , Gamal Hany , Elkatatny Salaheldin 
Published: October 2021

Abstract
The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters.
Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology.
The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation.
Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.
Real-Time Solution for Down Hole Torque Estimation and Drilling Optimization in High Deviated Wells Using Artificial Intelligence
Authors: Elzenary Mahmoud Nader 
Published: October 2021

ABSTRACTThis project provides a new realistic solution for the accuracy of down hole torque measurements using the integration of the Artificial intelligence (AI) technology with the downhole challenges being faced while drilling deep and high deviated wells. The new estimates are based on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate.This downhole torque model will be validated and verified through a real drilling scenario from a field in north of Africa. The field data includes weight on bit, surface torque, stand-pipe pressure, and rate of penetration were collected from the mentioned well which had experienced a costly stuck pipe situation. However, with the provided model the same encountered scenario will be avoided, due to the optimization of the real time drilling variables and hence, saving the well and evade a costly non-productive time.
Unlocking the Potential of Electrical Submersible Pumps: the Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization
Authors: Al Radhi Mohammed , Bermudez Fernando Angel , Al Madhoun Wael , Al Blooshi Khaled , Al Nahhas Noor Nasser , Shono Mohammed Nazeih 
Published: October 2021

Abstract
This paper is a summary of the collaborative work between ADNOC (Abu Dhabi National Oil Company) and nybl, a deep tech development company, and the results of applying nybl's proprietary "Science-Based Artificial Intelligence" to ADNOC Electrical Submersible Pump (ESP) wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of Artificial Intelligence (AI) / Machine Learning (ML) in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of ESPs. This paper will highlight ADNOC's innovative approach to pilot new technology through successful deployment on 27 wells, spread onshore and offshore, in real-time, with prescriptive actions.
Successful Application of Machine Learning to Improve Dynamic Modeling and History Matching for Complex Gas-Condensate Reservoirs in Hai Thach Field, Nam Con Son Basin, Offshore Vietnam
Authors: Hoang Son K. , Tran Tung V. , Nguyen Tan N. , Truong Tu A. , Pham Duy H. , Tran Trung N. , Trinh Vinh X. , Ngo Anh T. 
Published: October 2021

Abstract
This study aims to apply machine learning (ML) to make history matching (HM) process easier, faster, more accurate, and more reliable by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs and determining how LGR should be set up to successfully history match those production wells.
The main challenges for HM gas-condensate production from Hai Thach wells are large effect of condensate banking (condensate blockage), flow baffles by the sub-seismic fault network, complex reservoir distribution and connectivity, highly uncertain HIIP, and lack of PVT information for most reservoirs. In this study, ML was applied to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the HM process and the required LGR setup could also be determined. The proposed method helped provide better models in a much shorter time, and improved the efficiency and reliability of the dynamic modeling process.
500+ synthetic samples were generated using compositional sector models and divided into training and test sets. Supervised classification algorithms including logistic regression, Gaussian, Bernoulli, and multinomial Naïve Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as ANN were applied to the data sets to determine the need for using LGR in HM. The best algorithm was found to be the Decision Tree classifier, with 100% and 99% accuracy on the training and the test sets, respectively. The size of the LGR area could also be determined reasonably well at 89% and 87% accuracy on the training and the test sets, respectively. The range of the transmissibility multiplier could also be determined reasonably well at 97% and 91% accuracy on the training and the test sets, respectively. Moreover, the ML model was validated using actual production and HM data.
A new method of applying ML in dynamic modeling and HM of challenging gas-condensate wells in geologically complex reservoirs has been successfully applied to the high-pressure high-temperature Hai Thach field offshore Vietnam. The proposed method helped reduce many trial and error simulation runs and provide better and more reliable dynamic models.
Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence
Authors: Aldajani Saud K. , Alotaibi Saud F. , Abdulraheem Abdulazeez 
Published: October 2021

Abstract
The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir.
The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays.
In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir.
Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs.
After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN).
A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers.
This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.