bluuebunny/crossref_metadata_2025_split huggingface dataset:
History, Evolution, and Future of Casing Design Theory and Practice
Authors: Howard John A. , Trevisan Ruggero , McSpadden Albert , Glover Simon 
Published: September 2021

Abstract
Casing design and the associated load assumptions have evolved considerably over the last 30 years. The objective of this paper is to trace the history, evolution and future of casing design by means of the type of load cases and the assumptions made for them as it evolved from the early 1960's to the modern load case requirements for wells drilled in the 2020's. The vast majority of tubular failures in oil & gas wells are not attributable to computational errors in calculating design loads, but rather are due to a shortfall in considering the appropriate load scenarios. One common shortfall includes making incorrect or oversimplified assumptions for the initial and final temperature and pressure conditions. There is no industry standard for casing or tubing design loads, but there is an industry accepted standard process for the calculation of the stress on tubulars once the load cases are determined. Each operating company may use a different set of load assumptions depending on the well type and risk assessment.
This work also keeps in view the major computational tools used during each step change of the casing design evolution: slide rule/nomographs, HP 41C calculators, PC DOS and Windows programs, and the latest Cloud-Native paradigm with REST API's within a microservices architecture. A REST API (also known as RESTful API) is an Application Programming Interface (API) that conforms to the constraints of Representational State Transfer (REST) architectural style commonly used in current Cloud computing technology. The scope will also include ongoing research and development to address shortcomings of previous load case assumptions and calculations for extended reach and HPHT wells, closely spaced wells, and geothermal wells.
Modern wells and modern casing design load cases are in a constant state of evolution and casing failures will occur unless engineers and their tools also evolve.
Impact of Reservoir Fluid and Injection Gas on Shales Huff-N-Puff Performance in the Presence of Diffusion, Sorption, and Hysteresis
Authors: Enab Khaled , Emami-Meybodi Hamid 
Published: September 2021

Abstract
We assess the huff-n-puff performance in ultratight reservoirs (shales) by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, black oil) and different injection gases (CO2, C2H6, C3H8) by considering relative permeability hysteresis, diffusion, and sorption. A dual-porosity naturally fractured numerical compositional model is used that considers molecular diffusion and sorption to represent the flow mechanisms during the injection process. Killough's method, Langmuir's adsorption model, and Sigmund correlation are utilized to incorporate hysteresis, sorption, and diffusion, respectively. To investigate the impact of the fluid type, we consider three fluid types from Eagle Ford shale representing retrograde condensate, volatile oil, and black oil. We conduct a comprehensive evaluation of the impact of diffusion, sorption, and hysteresis on the production performance and retention of each fluid and injection gas. Eagle Ford formation is selected because it is the most actively developed shale, and it contains a wide span of PVT windows from dry gas to black oil.
The simulation results show that the huff-n-puff process improves the oil recovery by 4-6% when 10% PV of gas is injected. The huff-n-puff efficiency increases with reducing gas-oil-ratio (GOR) as oil recovery from low (GOR) reservoirs is doubled, while recovery from retrograde condensate increased by 20%. C2H6 provides the highest recovery for the black and volatile oil, and CO2 provides the highest recovery for retrograde condensate fluid type. Diffusion and sorption are essential mechanisms to be considered when modeling gas injection to any fluid type in shales. However, the relative permeability hysteresis effect is not significant. Neglecting diffusion during the huff-n-puff process underestimates the oil recovery and retention capacity. The diffusion effect on the oil density reduction is observed more during the soaking period. The diffusion impact increases with higher GOR reservoirs, while the sorption impact decreases with higher GOR. The retention capacity of the injected gas decreases with higher GOR. The diffusion impact on the retention capacity increases with higher GOR. Hence sorption and diffusion must be considered when modeling the huff-n-puff process in ultratight reservoirs.
Casing Twist Insight Through Fiber Cable
Authors: Samuel Robello , Wood Stuart , Olin Greg 
Published: September 2021

ABSTRACT
During perforating operations, identifying the orientation of fiber cable accurately is critical for maintaining the integrity of permanently installed fiber.Beyond completions,it alsoprovides insights into how the casings get twisted and how the mechanical stability of the casing is altered as the string is run in the hole. The drilling and completion system is as unique as the aspect ratio and length/diameter is very high. This puzzles the researchers in modeling forces, stresses, stretch, and twists.
To aid the accurate prediction in the position of the casing, radial orientation of downhole fiber optic cables canbe used. The clear images obtained by mapping the equipmentoutside thecasing provides not only how the casings get twisted after running in but also provide improved risk mitigation for perforating operations.The orientation angle of the casing versus depthis then analyzed to get the finaltwist and pitch of the twist of the casing. Several wells datawere analyzed to get a comprehensive view of the casingtwist as the casings were run and versus the model prediction.
The raw data obtained using the pulsed-eddy current time-domain decay at each station are used for the analysis. Each installed cable detection clamp (CDC) is placed above a casing centralizer located 2' above each joint of casing that had a clamp installed.This simplifies the process of locating the depth of each CDC. A casing collar locator easily identifies the casingjoints.Further, the data are used to find the casing rotation. Several wells showed normal casing rotation of 2–3 wraps along the lateral and onewell showed more than 12 wraps. Several reasons were considered and analyzed including the wellbore spiraling, borehole torsion,and additional mechanical forces applied duringrunning the casing. The coupling of the geometrical and mechanical twist and mechanical stability of the string are discussed in the paper withmathematical underpinnings. In thecase of abnormal prediction, additional mechanical forcesandgeometrical considerations were overlapped and comparedagainst the torque and drag model prediction.It has also beenfound that in some wells where the wellbore torsion washigh,it resulted in a complete twist of 360° atthe heel and in some cases negative trend.
Severe Casing Failure in Multistage Hydraulic Fracturing Using Dual-Scale Modelling Approach
Authors: Yu Hao , Dahi Taleghani Arash , Lian Zhanghua , Mou Yisheng 
Published: September 2021

Abstract
Field evidence of production logs after fracturing have documented the existence of abundant natural fractures in Weiyuan shale plays, which is widely acknowledged to have a positive impact on fracture network complexity. On the other hand, cases of severe casing failures have been frequently reported in this field during multistage fracturing jobs. Stress interference between two adjacent stages may intensify non-uniform loading on the casing string and accommodate failure. To better understand this problem, we establish a coupled 3D reservoir-scale model with complex well trajectory and tie it to a single well-scale model consisting of casing and the surrounding cement sheath. Using this model, we investigate the potential impacts of cement deficiency, clustered perforations, fracture geometry as well as spacing strategy on casing integrity. Our simulation results indicate that cement deficiency could intensify the load nonuniformity around the borehole which escalates the potential threats for casing failure. When cement deficiency reaches 45° along the minimum horizontal stress, it has the largest influence on the stress level in the casing. In addition, perforations could lower the casing strength, but the reduction may not change furthermore when the perforation diameter reaches a certain value. Moreover, impacts of fracture geometry and spacing on casing deformation are investigated. We conclude that the lower ratio of fracture length to its width and reasonable spacing strategy could help reduce the load non-uniformity on casing which avoid the casing deformation. The described workflow may be adopted in other areas to predict the possible casing failure problems induced by multistage hydraulic fracturing with cheap computational costs, to anticipate the challenges and avoid them by revisiting pumping schedule or spacing strategy.
Casing Wear: Prediction, Monitoring, Analysis and Management in the Culzean Field
Authors: Aichinger Florian , Brillaud Loic , Nobbs Ben , Couliou Florent , Oyovwevotu Joy , Mathieson Graeme , Vavasseur David , Hardie Jamie 
Published: September 2021

Abstract

Objectives/Scope
This paper will present predicted vs. measured wear for six wells that were analysed in the Culzean field, which is a high-pressure, high-temperature (HPHT) gas condensate field located in the central North Sea.
The focus rests on the casing wear prediction, monitoring and analysing process and within that, especially on how to make use of offset well data to improve the accuracy of casing wear predictions.

Methods
The three major inputs to successfully predict casing wear are: Trajectory & tortuosity, wear factor and required rotating operations. All those were calibrated based on field measurements (High-resolution gyro, MFCL (Multi-Finger-Caliper-Log) and automatically recorded rig mechanics data), to improve the prediction quality for the next section and/or well.
The simulations were done using an advanced stiff-string model featuring a 3D mesh that distinguishes the influence of different contact type and geometry on the wear groove shape.
The "single MFCL interpretation method", in which the wear is measured against the most probable elliptical casing shape and herby allowing wear interpretation with only one MFCL log and avoiding bias error, was applied. (Aichinger, 2016)

Results, Observations, Conclusions
For the six wells that were analysed the prediction of the largest wear peak per well section was compared to the measurement. In the planning phase (before any survey data was available) the mean absolute error on the wear groove depth was +/- 0.018 [in] (+/- 0.46 [mm]), the maximum error was + 0.045 [in] (+ 1.1 [mm]). The error of the results is summarized in Figure 10 and laid out in detail in Figure 9. Generally, the predictions are accurate enough to be able to manage casing wear effectively.
In this case, the maximum allowable wear on the intermediate casing was extremely limited to ensure proper well integrity in case of a well full of gas event while drilling an HTHP reservoir.

Novel/Additive Information
This paper should provide help to engineers who seek to improve the accuracy of casing wear prediction and hence improve casing wear management. It presents a new way of anticipating tortuosity based on offset well data and it offers a suggestion on how to deal with MFCL measurement error during wear factor calibration and wear prediction.
Practical Applications of Diagnostic Data Science in Drilling and Completions
Authors: Senters Chad , Jayakumar Swathika , Warren Mark , Wells Mike , Harper Rachel , Leonard Richard , Woodroof Robert 
Published: September 2021

Abstract
The application of data science remains relatively new to the oil and gas industry but continues to gain traction on many projects due to its potential to assist in solving complex problems. The amount and quality of the right type of data can be as much of a limitation as the complex algorithms and programing required. The scope of any data science project should look for easy wins early on and not attempt an all-encompassing solution with the click of a button (although that would be amazing). This paper focuses on several specific applications of data applied to a sizable database to extract useful solutions and provide an approach for data science on future projects.
The first step when applying data analytics is to build a suitable database. This might appear rudimentary at first glance, but historical data is seldom catalogued optimally for future projects. This is especially true if specific portions of the recorded data were not known to be of use in solving future problems. The approach to improving the quality of the database for this paper is to establish requirements for the data science objectives and apply this to past, present and future data. Once the data are in the right "format", the extensive process of quality control can begin. Although this part of the paper is not the most exciting, it might be the most important, as most programing yields the same "garbage in = garbage out" equation. After the data have found a home and are quality checked, the data science can be applied.
Case studies are presented based on the application of diagnostic data from an extensive project/well database. To leverage historical data in new projects, metrics are created as a benchmarking tool. The case studies in this paper include metrics such as the Known Lateral Contribution (KLC), Heel-to-Toe Ratio (HTR), Communication Intensity (CI), Proppant Efficiency (PE) and stage level performance. These results are compared to additional stimulation and geological information.
This paper includes case studies that apply data science to diagnostics on a large scale to deliver actionable results. The results discussed will allow for the utilization of this approach in future projects and provide a roadmap to better understand diagnostic results as they relate to drilling and completion activity.
Autonomous Control of Well Downtime to Optimize Production and Cycling in Sucker Rod Pump Artificially Lifted Wells
Authors: Nickell Ian , Treiberg Terry 
Published: September 2021

Abstract
For decades sucker rod pump artificially lifted wells have used devices called pump off controllers (POC) to match the pumping unit's runtime to the available reservoir production by idling the well for a set time where variable frequencies drives are not available. In doing this the POC allows the well to enter a set period of downtime when the downhole pump fillage is incomplete to avoid premature failures, and then brings the well back online to operate before production is lost. Although this method has been successful for several years, autonomous control algorithms can be utilized to reduce failures or increase production in cases where the downtime is not already optimized. Optimizing the idle time for a sucker rod pump artificially lifted well involves understanding the amount of time required to fill the near wellbore storage area before generating a fluid column above the pump intake that will begin to hinder inflow from the reservoir into the wellbore. By varying the idle time and observing the impact on production and cycles the program hunts for the optimal idle time. By constantly hunting for the optimal idle time the optimization process can adjust the idle time when operating conditions change. This gives the advantage of always meeting the current well bore and reservoir conditions without having to have a user make these changes and determine what the downtime for the well is. Autonomously modulating the idle time for a well, if done properly will either reduces incomplete fillage pump strokes, in cases where the idle time is too short, or will increase the wells production in cases where the idle time is too long. Overall this will result in the optimization of wells by reducing failures and/or increasing production, generating a huge value to the end user by automating the entire process of downtime optimization.
A Graph Network Based Approach for Reservoir Modeling
Authors: Sun Wenyue , Sankaran Sathish 
Published: September 2021

Abstract
Reservoir management routinely requires assimilating historical data and predicting field performance against multiple production strategies before implementing them in the field. However, traditional numerical methods are often cumbersome to characterize, build and calibrate at a timescale that can be used reliably for such short-term decision cycles such as production forecasting, IOR optimization and production rate control. Simpler analytical models make assumptions and lack the rigor needed to adequately model these systems. Pure data-driven methods may lack physical insights or have limited range of applicability. Model fidelity, speed, interpretability, suitability to automate and ease-of-use are some key modeling traits that are desired for reservoir management purposes.
In this work, we propose to use a reservoir graph-network modeling approach (RGNet), based on the concept of diffusive time of flight, to forecast well performance using routinely measured field measurements (e.g. bottomhole pressure and rates). We propose a novel, model order reduction method based on discretized time of flight for multiple wells with interference. It simplifies the 3D reservoir flow problem into a flow network representation that can be solved as a 2D simulation model with any general-purpose reservoir simulator. Parameters in RGNet model cover well productivity index, grid pore volume and transmissibility, which are estimated through a history-matching process. After history matching, multiple posterior RGNet models are generated to quantify subsurface uncertainties. The RGNet modeling approach allows various fluid-flow physics to be modeled within the grids and boundary conditions, and is applicable to a range of conventional and unconventional reservoirs with different flow mechanisms.
We applied the proposed approach on a field case reservoir models for multiple wells with interference. By virtue of the reduced complexity, the modeling methodology is highly scalable and still retains physical interpretability. The calibration method produces parsimonious models and provides uncertainty estimates in history matching parameters with range of outcomes. In addition, the RGNet models are much faster to simulate, over 1000x speed up, compared with full-physics models. We then used RGNet models for well-control and flood optimization and achieved significant improvement over field net-present-values.
Parameterization of the proposed reservoir graph-network modeling approach provides a unique and sustainable way to reduce model complexity needed for reservoir management purposes. The method is rooted in physical principles and provides an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.
Well Completion Optimization in Unconventional Reservoirs Using Machine Learning Methods
Authors: Baki Sohrat , Temizel Cenk , Dursun Serkan 
Published: September 2021

Abstract
Unconventional reservoirs, mainly shale oil and natural gas, will continue to significantly help meet the ever-growing energy demands of global markets. Being complex in nature and having ultra-tight producing zones, unconventionals depends on effective well completion and stimulation treatments in order to be successful and economical. Within the last decade, thousands of unconventional wells have been drilled, completed and produced in North America. The scope of this work is exploring the primary impact of completion parameters such as lateral length, frac type, number of stages, proppant and fluid volume effect on the production performance of the wells in unconventional fields.
The key attributes in completion, stimulation, and production for the wells were considered in machine learning workflow for building predictive models. Predictive models based on Neural Networks, Support Vector Machines or Decision Tree Based ensemble models, serves as mapping function from completion parameters to production in each well in the field. The completion parameters were analyzed in the workflow with respect to feature engineering and interpretation.
This analysis resulted in key performance indicators for the region. Then the optimum values for the best production performing completions were identified for each well. Predictive models in the workflow were analyzed in accuracy and best model is used to understand the impact of completion parameters on the production rates. This study outlines an overall machine learning workflow, from feature engineering to interpretation of the machine learning models to quantify the effects of completion parameters on the production rate of the wells in unconventional fields
Advanced Modeling Capability to Enhance Near-Wellbore and Far-Field Bridging in Acid Fracturing Field Treatments
Authors: Khan Abdul Muqtadir , Emelyanov Denis , Romanovskii Rostislav , Nevvonen Olga 
Published: September 2021

Abstract
Different applications of fracture bridging and diversion are used regularly in carbonate acid fracturing without an in-depth understanding of the physical phenomena that dominate the processes involved in the bridging and diversion process. The extension of modeling capabilities in conjunction with yard-scale and field-scale experiences will increase our understanding of these processes.
A robust multimodal diversion pill and polylactic acid fiber-laden viscous acid were utilized for near-wellbore and far-field bridging, respectively. Numerous field treatments demonstrated the uncertainty of achieving effective diversion. An existing multiphysics model was extended to develop functionalities to model diversions at different scale. Extensive laboratory testing was conducted to understand the scale of bridging and diversion mechanisms. Finally, a bridging yard test was designed, and field case studies were used to integrate all the branches.
Field cases showed a diversion pressure up to 4,000 psi depending on perforation strategy, pill volume, and pill seating rate. Correlations showed the interdependence of multiple parameters in diversion processes. The field studies motivated modeling capabilities to simulate the critical diversion processes at high resolution and quality. The model simulates diverting agents that reduce leakoff in the fracture area and their effects on fracture geometry. The approach considers the acid reaction kinetics coupled with geomechanics and fluid transport. Different diverting agent concentrations required for bridging can be modeled effectively. A yard test was designed to confirm the integrity of the pill material through completion valves (minimum inside diameter 9.5 mm) and analyzed with high-resolution imaging. All the theoretical, mathematical, and numerical findings from modeling were integrated with laboratory- and yard-scale experimentation results to develop and validate near-wellbore and far-field diversion modeling. Analytical correlations were formulated from injection rate, particulate material concentration, pill volumes, fracture width, etc., to incorporate and validate the model.
This study enhances understanding of the different diversion mechanisms from high-fidelity theoretical modeling approach integrated with a practical experimental view at laboratory and field scale. Current comprehensive research has significant potential to make the modeling approach a reliable method to develop tight carbonate formations around the globe.