bluuebunny/crossref_metadata_2025_split huggingface dataset:
Water-Oil-Gas Settling Interface Calculation in Shut-In for Undulating Well
Authors: Kang Yongfeng , Gonzales Adolfo , Samuel Robello , Liu Zhengchun 
Published: October 2020

Abstract
Modern directional wellbore trajectories are more complex now with some wells having an undulating trajectory. When installing artificial lift devices such as an electric submersible pump (ESP), the pump must be submerged in the liquid phase of the hydrocarbon in order to avoid gas-lock issues, including in shut-in conditions. Incorrect placement of such device would require a very costly remedy. In an undulating trajectory well, it is possible that the fluid in the well is in the gas phase at some of the downhole upper elbow locations during shut-in. This paper presents a method to calculate the water/oil/gas settling interface during shut-in conditions for an undulating trajectory well.
In this method, the well is divided into multiple uphill and downhill sections. For each section, it is assumed that the GOR (Gas Oil Ratio) and WOR (Water Oil Ratio) are the same as the production GOR and WOR. The pressure at the elbow turning point of two uphill and downhill sections is assumed to be the same to ensure the pressure continuity. Then, by the given surface or bottom (reservoir) pressure, the gas/oil and oil/water interfaces are calculated with iterations with a numerical method to match the GOR and WOR considering the wellbore pressure and temperature effect for each section. Once all the sections are calculated, the final results are combined to achieve the global gas/oil and oil/water interfaces and the pressure and temperature profiles.
This method has been implemented in an advanced casing and tubing design application to calculate the well pressure and temperature profiles, and then these profile results are applied later for further stress analysis. A typical undulating well is presented for case study to explain the calculation steps and results. As expected, the gas/oil and oil/water interfaces are obtained where the water is settling at the lower elbow of the well, the gas phase is accumulated at the upper elbow of the well, and the oil phase stays in the middle between the gas and water. The pressure profile is achieved showing different pressure gradients of the gas/oil/water settling intervals and pressure is continuous along the whole well. The temperature profile is also obtained, showing different temperature behavior for gas, oil, and water intervals with different shut-in duration, which is useful for stress analysis in the string at different depths.
This paper fills a gap that has not yet been discussed in the industry literature. It is an important for field application, especially for choosing the artificial lift device installation depth. Further, the calculated pressure and temperature profiles are useful for casing and tubing design stress analysis purposes.
Improving Casing Running Operation Success in Long Laterals by Using Bottom Hole Assembly Tripping Load Analysis
Authors: Baker Alexa , Khochbar Namig , Leonard Benjamin , Tan Hui Xian 
Published: October 2020

Abstract
As horizontal wells have come to dominate shale and tight development programs, US land rigs have experienced challenges with running production casing on elevators to total depth (TD) in long laterals (≥10,000 ft). An initial study was done, and the results identified a relationship between the level of open hole drag when tripping out with the final lateral bottom hole assembly (BHA) and the success rate of casing running operations. The study results led to further investigation to determine if there was a correlation that could be applied more broadly to improve overall performance. Front end engineering work included analyzing offset data to establish the baseline of successful casing running operations and building engineering models to evaluate the level of open hole drag. The level of open hole drag was determined by modeling the hookload profile when tripping out with various friction factors, characterizing the hookload signatures, and building a correlation with real-time hookload data. A workflow was then developed with a focus on monitoring the first five to ten stands tripped out of the hole to determine if additional hole cleaning was necessary based on the observed real-time friction factor. This methodology and workflow have been used successfully to support numerous operations in the Permian and Marcellus Basins. As a result, the success rate of running casing on elevators to bottom in these two areas have improved significantly, the results for the Permian Basin can be seen in Fig. 1.
Difference-Value Plotting Function for Pressure Transient Analysis in Low-Permeability Reservoirs
Authors: Luo Le , Lee John , Blasingame Thomas 
Published: October 2020

Abstract
This paper proposes the difference-value plotting function (DVPF) for the diagnostic analysis and interpretation of pressure transient test data in low-permeability reservoirs. Specifically, this work uses the approximation of the analytical solution for the performance of a vertical well with a single finite conductivity vertical fracture, where a Taylor Series expansion is used to obtain an asymptotic solution for early-time flow, which includes terms for wellbore storage and fracture conductivity. The well-testing derivative of this result is then obtained and is of a similar form.
By subtracting the derivative form from the pressure form, we remove the "dominant" wellbore storage term from the asymptotic solution. We then need to normalize that difference by the square root of time (or dimensionless time) to obtain the final formulation of the DVPF which leaves a single constant parameter multiplied by time on the right-hand-side. Our contention is that this formulation leaves us with a diagnostic plotting function which provides a unique and contrasting behavior compared to using the pressure drop and/or pressure drop derivative functions alone for diagnostics and interpretations.
As is typical of pressure transient or well testing data at early times, the observed pressures often exhibit random data noise. As such, we have adapted a noise reduction algorithm that was originally used for signal processing to smooth both the pressure and derivative functions.
Lastly, we demonstrate the difference-value plotting function (DVPF) on several cases of synthetic and field-derived data to illustrate the utility of this methodology. Specifically, we have applied this method to cases in which it is difficult to determine unique interpretations using traditional methods (e.g., insufficient duration tests, lengthy WBS distortion, and effects of ultra-low permeability). The proposed DVPF allows us to observe underlying characteristics that are obscured at early times in traditional pressure and derivative analysis, and for the demonstration examples provided in this work, the DVPF does provide a strong auxiliary means of interpretation.
The Impact of Practical Experiences on the Development of Petroleum Engineering Education
Authors: Retnanto Albertus , Alyafei Nayef , Fadlelmula Mohamed , Sheharyar Ali 
Published: October 2020

Abstract
The practical application of petroleum principles is the key to optimum petroleum engineering education. Although some of these skills are required and often emphasized by accreditation bodies, mastering them requires vision, planning, and allocation of time and budget by academic institutions. Enhancing the efficiency and effectiveness of teaching and active learning of practical relevance can be achieved through applying information technologies and performing undergraduate research experience program. Research experiences support students in comprehending a phenomenon in an area that contains practical experiences, specifically in petroleum engineering. In Qatar, a national funding initiative called the undergraduate research experience program (UREP) is available, and luckily almost all the petroleum engineering students at Texas A&M University at Qatar (TAMUQ) participate in UREP. Research provides an iterative process to develop how knowledge evolves. Not only that, but several field trips are arranged annually for the students to visit onshore rigs and facilities in the state of Qatar. Adding to that, in-house virtual reality (VR) offshore rig and geological site were developed that can waive several difficulties in arranging an actual visit. The information technologies bridge formal and informal learning by connecting the students to real-world experiences. In this paper, surveys were conducted among former students on the value of independent student research on developing their skills. Also, the value of actual and virtual field trips on enhancing their understanding of petroleum engineering concepts were measured through the conducted surveys. The results of surveys indicate that the provided experiences significantly enhanced both the skills and learning experiences in the petroleum engineering education of former students in their workplace. Feedback came from the students that research experiences developed the students' skills that complemented their workplace skills. Also, recommendations were made to embed the VR field trip in more petroleum engineering courses. Moreover, concepts in courses such as Drilling and Production Engineering, as well as Geology of Petroleum, became easier to grasp after these trips.
A 3D Acid Transport Model for Acid Fracturing Treatments With Viscous Fingering
Authors: Dong Rencheng , Wheeler Mary F. , Ma Kang , Su Hang 
Published: October 2020

Abstract
In acid fracturing treatments, the goal is to create enough fracture roughness through differential acid etching on fracture walls such that the acid fracture can keep open and sustain a high enough acid fracture conductivity under the closure stress. The viscous fingering phenomenon has been utilized in acid fracturing treatments to enhance the differential acid etching. For relatively homogeneous carbonate reservoirs, by injecting a low-viscosity acid into a high-viscosity pad fluid during acid fracturing, the acid tends to form viscous fingers and etch fracture surfaces non-uniformly. In order to accurately predict the acid- fracture conductivity, a detailed description of the rough acid-fracture surfaces is required. In this paper, we developed a 3D acid transport model to compute the geometry of acid fracture for acid fracturing treatments with viscous fingering. The developed model couples the acid fluid flow, reactive transport and rock dissolution in the fracture. Our simulation results reproduced the acid viscous fingering phenomenon ob-served from experiments in the literature. During the process of acid viscous fingering, high-conductivity channels developed in the fingering regions. We performed parametric studies to investigate the effects of pad fluid viscosities and acid injection rates on acid fracture conductivity. We found that a higher viscosity pad fluid and a higher acid injection rate help acid to penetrate deeper in the fracture and result in a longer etched channel.
A Shattered Visage and Choke Point Dilemma for Petroleum Engineers
Authors: Samuel Robello 
Published: October 2020

Abstract
The 1980s oil industry crash, which was caused by an increase in production and a slowdown of the world economy, resulted in a huge surplus of crude oil. Improved horizontal drilling, completion technologies and the underpinning of engineering advancements were major contributing factors. The crude oil price has continued to fall since mid-2014, and has dropped from $106 per barrel. This paper examines the supply and demand of the future workforce and the warning signs that were ignored before the crash.
The framework of questions to be addressed includes the following: "What is the fundamental problem in the industry and petroleum universities?", "Is it throttling the advancement of the petroleum industry?", and "What are the changes needed to meet the shortage of future engineers?" Although several challenges are inherently present, the article will particularly ponder the following primary challenges: talent pipeline and shortage, educational curriculum drought, petroleum education intake reform, academia-industry tighter participation, knowledge base erosion, and technical talent flight.
A surge in university enrollment has tilted the balance of the student and aging faculty ratio. The key takeaways from this paper include the challenge areas in education and how the industry is responding to its needs, outlining the emerging future direction to meet the challenges of the cyclical industry, as well as climate change pressures. This paper presents the future state of the petroleum program, which will be in a state of stunned growth similar to other industries, like mining and nuclear energy. It also shows a link to environmental pressures.
Quantification and Incorporation of Geomechanical Risks in Optimization of Geologic CO2 Storage Using Coupled-Physics Models
Authors: Zheng Fangning , Jahandideh Atefeh , Jha Birendra , Jafarpour Behnam 
Published: October 2020

Abstract
Numerical flow simulation models for CO2 injection, transport and trapping mechanisms have been used to optimize geologic CO2 storage (GCS) by improving trapping efficiency and injection strategies. Injection of CO2 into geologic formations can cause geomechanical changes and lead to reservoir expansion, ground surface uplift, and induced seismicity. Such deformations create geomechanical risks associated with GCS, which can compromise the safety and storage capacity of CO2. The geomechanical risks of CO2 sequestration have drawn significant attention in recent years due to considerable ground uplifting and induced microseismic activities that have been reported in several field cases. However, studies in this area have not considered the effect of geomechanical risks in optimizing the storage performance. We present an optimization framework to strategically place CO2 injection wells to maximize the storage capacity and minimize the associated geomechanical risks. To this end, we perform the optimization using three-dimensional coupled flow and geomechanical models, by applying the Mohr-Coulomb plastic failure criterion to model mechanical rock failure risk. Additionally, the geomechanical simulation results are used to quantify the risk associated with ground surface displacement and plastic strain, which are extracted from the simulation outputs. A multi-objective optimization problem is formulated to maximize CO2 storage while minimizing the two forms of geomechanical risks. The injection well locations are defined as decision variables and Genetic Algorithm is implemented to solve the multi-objective optimization problem. The solution of the proposed framework leads to nontrivial optimal decisions, which are different from the case where geomechanical risks associated with CO2 injection are ignored. We find that the wells may not necessarily be concentrated in areas with the highest storage capacity because that may lead to rock failure and unacceptable levels of ground surface uplift. Instead, the maximum storage is limited to keep the reservoir in the elastic state based on the specified reservoir geomechanical and flow properties. We also observe that well interactions during CO2 injection can cause shear failure of the formation between the wells. These observations suggest that accounting for geomechanical risks associated with CO2 injection is necessary to design a sustainable plan for CO2 injection and storage. The paper ends with a discussion about the importance of accounting for the uncertainty in rock properties and the resulting model predictions in optimizing the well locations.
Production Operations Efficiency Gains Enablers: Machine Learning or Physics Based Models?
Authors: Joseph Ayoub , Michael Thambynayagm , Jose Jimenez 
Published: October 2020

Abstract
Industry reports confirm that while the unconventional wells drilling and completion operations have achieved significant efficiency gains, which translated into steep cost reductions, the production operations costs remained largely flat. The authors provide a quick review of the above and discuss technologies aimed at addressing this situation including machine learning, AI and physics-based modeling. The latter has been the cornerstone for production optimization since the early days of oil and gas exploitation, however, it has been uneconomical for systematic use in unconventional wells and is applied to only a small fraction of the large number of active wells. This is mainly due to their complex completions, which makes modeling a tedious and resource intensive task. Data science promises faster and nimbler solutions, however, while advanced techniques such as deep learning have achieved impressive advances in many areas like face and speech recognition, they remain mostly rooted in pattern recognition, with no grasp of cause and effect, which limits the type of problems they can successfully resolve. This paper will discuss the above solutions and limitations and presents a novel physics-based approach that would be economical for widespread deployment.
Role of Intermolecular Forces on Surfactant-Steam Performance Into Heavy Oil Reservoirs
Authors: Seng Lee Yeh , Hascakir Berna 
Published: October 2020

Abstract
This study investigates the role of heavy oil polar fractions in surfactant-steam flooding performance. Performance analyses were done by examination of the dipole-dipole and ion-ion interactions between polar head group of surfactants and charged polar fraction of crude oil, asphaltenes.
Surfactants are designed to reduce the interfacial tension between two immiscible fluids (such as oil and water) and effectively used for oil recovery. They reduce the interfacial tension by aligning themselves at the interface of these two immiscible fluids, this way, their polar head group can stay in water and non-polar tail can stay in oil phase. However, in heavy oil, the crude oil itself has high amount of polar components (mainly asphaltenes). Moreover, polar head group in surfactants is charged and the asphaltene fraction of crude oils carry reservoir rock components with charges. The impact of these intermolecular forces on surfactant-steam process performance was investigated with 10 coreflood experiments on an extra-heavy crude oil. 9 surfactants (3 anionic, 3 cationic and 3 nonionic surfactants) were tested. Results of each coreflood test were analyzed through cumulative oil recovery and residual oil content. The performance differences were evaluated by polarity determination through dielectric constant measurements and by ionic charges through zeta potential measurements on asphaltenes fraction of produced oil and residual oil samples.
The differences in each group of surfactant tested in this study are the tail length. Results indicate that longer hydrocarbon tail yielded higher cumulative oil recovery. Based on the charge groups present in the polar head of surfactants anionic surfactants resulted in higher oil recovery. The further examinations on asphaltenes from produced and residual oils show that the dielectric constants of asphaltenes originated from the produced oil gives higher polarity for surfactant-steam experiments conducted with longer tail length, which provide information on polarity of asphaltenes. The ion-ion interaction between produced oil asphaltenes and surfactant head groups were determined through zeta potential measurements. For the most successful surfactant-steam processes, these results showed that the changes on asphaltenes surface charges getting lower with the increase in oil recovery, which indicates that once asphaltenes are interacting more with polar head of surfactants, then, the recovery rate increases.
Our study shows that surfactant-steam flooding performance in heavy oil reservoirs controlled by the interaction between asphaltenes and polar head group of surfactants. Accordingly, main mechanism which controls the effectiveness of process is the ion-ion interaction between the charges in asphaltene surfaces and polar head group of crude oils. Since crude oils carry mostly negatively charged reservoir rock particles, our study suggests the use of anionic surfactants for the extraction of heavy oils.
Physics-Driven Machine Learning-Based Borehole Sonic Interpretation in the Presence of Casing and Drill Pipe
Authors: Liang Lin , Lei Ting , Donald Adam , Blyth Matthew 
Published: October 2020

Abstract
Interpretation of sonic data acquired by logging-while-drilling tool or wireline tool in cased holes is complicated by the presence of drill pipe or casing because those steel pipes can act as a strong waveguide. Traditional solutions, which rely on using frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals co-exist in the same frequency band or arrival-time range. We hence develop a physics-driven machine learning-based method to overcome the challenge. In this method, two synthetic databases are generated from a general root-finding mode-search routine based on two assumed models, one is defined as a cemented cased hole for wireline scenario, another with a steel pipe immersed in a fluid-filled borehole for the logging-while-drilling scenario. The synthetic databases are used to train neural network models, which are first used to perform global sensitivity analysis on all relevant model parameters so that the influence of each parameter on the dipole dispersion data can be well understood. A least-squares inversion scheme utilizing the trained model was developed and tested on synthetic cases. The scheme showed good results and a reasonable uncertainty estimate was made for each parameter. We then extend the application of the trained model to develop a method for automated labeling and extraction of the dipole flexural dispersion mode from other disturbances. The method combines the clustering technique with the neural network model-based inversion and an adaptive filter. Testing on field data demonstrates that the new method is superior to traditional methods because it introduces a mechanism from which unwanted pipe mode can be physically filtered out.
This novel physics-driven machine learning-based method improved the interpretation of sonic dipole dispersion data to cope with the challenge brought by the existence of steel pipes. Unlike data-driven machine learning methods, it can provide global service with just one-time offline training. Compared with traditional methods, the new method is more accurate and reliable because the processing is confined by physical laws. This method is less dependent on input parameters; hence a fully automated solution could be achieved.