bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Integrated Approach to Optimize Drilling, Completion and Hydraulic Fracturing Performance in Unconventional Wells
Authors: Omer Mohammed , Fragachan Francisco E. 
Published: October 2020

Abstract
In the age of artificial intelligence, digitalization, rising energy demand, falling prices of barrel of oil and increasing difficulty in oil & gas recovery we need to have an integrated approach based on physics, artificial intelligence and rock mechanics to reduce the non-productive time in drilling and -in parallel- enhance well production. The integrated approach should help in reducing cost, minimize human intervention, reduce drilling associated risks, minimize the negative impact on near wellbore rock behavior due to stimulation and enhance the recovery of hydrocarbons.
Stuck pipe is a major stake holder in "non-productive time" and is estimated to cost the oil and gas industry around $250 to $300 Million a year. Stuck pipe due to wellbore stability issues is a regular phenomenon while drilling weak zones in minimum stress direction especially in Middle East. William Lyon's in 2010, estimated that cost of stuck pipe in deep oil and gas wells is around 25% of overall budget.
To counter stuck pipe, for instance, drilling engineer may decide to increase the mud weight inorder to minimize the wellbore stability issues, and this could enhance challenges to a stimulation engineer associated with potential damage. Simliarly, an improper acidizing could soften the rock and negatively impact mechanical response of near wellbore rock during production. Two simple examples demonstrate the value of an engineering holistic approach based on wellbore stability integration into hydraulic fracturing treatment design considering the complexity involved around geomechanics.
This study introduces a workflow that holistically integrates a rock mechanics approach to optimize drilling performance and characterize the stresses around the wellbore with the completion design, combining the geomechanical and petrophysical properties to optimize the completion and stimulation design. This engineering workflow will enable to design and customize a particulate diverter system for effective fluid diversion and wellbore coverage by uniformly distributing the stimulation fluid with an aim to create fracture network complexities, enhancing the production. Additionally, this paper showcases the learnings from various field case histories including but not limited to drilling across weak bedding plane from Asia, wellbore stability issues in Middle East that resulted in high non-productive time from drilling, Uniform Fracture Growth from Horizontal Wells and re-fracturing strategies from North and South America.
This approach will enable optimizing well performance from drilling to production, minimize risks and optimize intervention by retro alimenting each phase of the process to the next. This workflow provides a innovative strategic approach optimizing drilling, completion and stimulation mitigating challenges in unconventional formations that can be extrapolated to conventional reservoirs as well.
On Some Factors Affecting Casing Collapse Resistance under External Pressure
Authors: Lin Bisen , Coe David , Harris Richard , Thomas Timothy 
Published: October 2020

Abstract
Casing collapse under external pressure is a complex phenomenon and is one of the governing factors in the tubular design of oil and gas wells. Collapse strength of a casing depends on its outer diameter to wall thickness ratio (i.e. D/t), material Young's modulus, yield stress, shape of stress-strain curve, temperature, ovality, wall eccentricity, circumferential residual stress, as well as combined loadings. This paper studies the quantitative effects of two geometric impefections, namely (i) wear on the inside diameter (ID) of casing and (ii) wall eccentricity angle; as well as two combined loading scenarios: (iii) dogleg bending and (iv) axial compression. Although these scenarios are not covered in the industry Standards, such as API TR 5C3 (June 2018 edition) [1], they are important factors that must be considered in the tubular design of oil and gas wells.
In order to quantify the effects of the four scenarios listed above, nonlinear parametric collapse Finite Element Analysis (FEA) studies were performed. Modified Riks method was utilized to predict the casing on-site collapse pressure as well as the unstable post-collapse response. Both material and geometric nonlinearities were taken into account. Elastic-plastic material property with strain hardening was incorporated in the collapse FEA models.
Based on the results of the parametric FEA simulations, some important observations and conclusions can be made: (i) for the scenario of casing ID wear, reduction in worn casing collapse resistance is nearly proportional to the reduction in minimum worn thickness regardless of the casing dimensiosns and material grade; (ii) for the case of wall eccentricity irregularity, as the circumferential location of maximum wall thickness moves closer to the minimum wall thickness location, the casing collapse resistance decreases; (iii) casing collapse resistance decreases as the dogleg severity (DLS) increases. Moreover, the effect of dogleg bending on collapse resistance has a strong dependence on casing dimensions and material grade; (iv) in contrast to the effect of axial tension, dependence of collapse resistance on axial compression is quite nonlinear and non-monotonic. In other words, as axial compression increases, collapse resistance increases until the axial compression reaches approximately 50% to 60% (primarily depending on D/t ratio) of yield strength, and then decreases as axial compression increases further.
Carbonate Caprock-Brine-CO2 Interaction: Alteration of Hydromechanical Properties
Authors: Sang Guijie , Liu Shimin 
Published: October 2020

Abstract
Caprocks play a crucial role in geological storage of CO2 by preventing the escape of CO2 and thus trapping CO2 into underlying porous reservoirs. An evaluation of interaction-induced alteration of hydromechanical properties of caprocks are essential to better assess the leaking risk and injection-induced rock instability, and thus ensuring a long-term viability of geological CO2 storage. We study the changes in nanopores, elastic velocities and mechanical responses of a carbonate caprock due to rock-water/brine-CO2 interaction (CO2 pressure ~ 12 MPa; 50 ℃). Before the interaction, the total and accessible porosities are 1.6% and 0.6%, respectively, as characterized by the Small Angle Neutron Scattering (SANS) technique. SANS results show that the total porosity of the carbonate caprock increases apparently due to rock-brine-CO2 interaction and the increasing rate rises as brine concentration increases (2.2% for 0M NaCl, 2.6% for 1M NaCl, and 2.7% for 4M NaCl). The increase total porosity is due to the dissolution of calcite which tends to enlarge accessible pores (by 0.8%-1.2%) while slightly decrease the inaccessible pores (by 0.1%-0.2%). Under CO2-acidified water environment, P- and S-wave velocities (5536.7 m/s and 2699.7 m/s) of a core sample containing natural fractures decreases by 8.5% and 8.1% respectively, while both P- and S-wave velocities (6074.1 m/s and 3858.8 m/s) for a intact sample show only ~0.5% decreases. The interaction also causes more than 50% degradation of the uniaxial compressive strength for the core sample with natural fractures.
We also conduct simulations of the single-phase creeping flow and two-phase water-CO2 flow in micron-scale natural fractures, as extracted from X-ray Micro-CT images of the core sample. The simulated absolute permeability (2.0×10-12 m2) is much higher than the matrix permeability (6.7×10-20 m2before the interaction; 1.3×10-19 m2after the interaction), as calculated based on the Kozeny–Carman Equation. This indicates that natural fractures provide preferential flow paths for CO2 while flow through caprock matrix can be reasonably neglected. Simulation results also indicate that CO2 preferentially migrates in the natural fractures where there are more inter-connected and permeable channels. The study recommends that more attention should be addressed on interaction-induced alteration of fracture/faults permeability/stability, and its effect on the sealing integrity of carbonate caprocks.
Maximizing the Effectiveness of Global Virtual Teams
Authors: Holding Eagle L. M. 
Published: October 2020

Abstract
Global virtual teams (GVTs) are an ever-expanding reality in today's oilfield. In comparison to a traditional team in which individuals are co-located and regularly work face-to-face to accomplish their goals, GVTs work while separated by time, space and even culture. Their use allows organizations to leverage a concentrated group of technical experts in combination with local teams. However, GVTs operate differently than co-located teams. When organizations assume otherwise, they are likely to be disappointed with the results.
This paper consists of a review of scholarly literature along with news sources, observations and interviews with individuals who work in GVTs in the oilfield.
Maximizing the benefit of GVTs requires organizations to acknowledge and accommodate the manners in which global virtual teams differ from co-located teams. When this is not done, teams will lack efficiency, yield poorer performance, and result in a less satisfied workforce. While personal leadership styles such as transformational leadership are often found to be most effective for co-located teams, shared leadership as well as styles that are less relationship-based may be more effective when face-to-face interactions are less accessible. Much of the communication in global virtual teams is computer-mediated and asynchronous, for example, by email. Without body language, tone of voice, and real-time reactions, this kind of communication often invites misunderstandings and fails to convey team member intent. This can be especially challenging during inter-cultural communication. Technologies such as phone calls, web- and video-conferencing, and even augmented reality can enrich and improve the effectiveness of communication. Flexible scheduling can also help teams who are separated by multiple time-zones. Finally, team cohesion and effectiveness can be substantially improved through judicious use of travel for working site visits where teams have a chance to work together in real-time as well as engage in personal social interactions. Increasing the intentionality with which organizations support and manage their GVTs has the potential to substantially increase their effectiveness.
Despite the prevalence of GVTs in oilfield organizations, a search on this topic in the OnePetro oilfield literature database yields few results. This paper describes many of the challenges that exist within global virtual teams that are less likely to be present within co-located teams. It also proposes leadership strategies, technology, and other methods that organizations can use to overcome these challenges and maximize the effectiveness of their GVTs.
Lessons Learned From In-Bit Weight and Torque Measurement
Authors: Vempati Chaitanya , Moss William , Townsend Todd , Schandorf Cecil , Evans Kenneth 
Published: October 2020

Abstract
During the last decade, a tremendous amount of work has been published on using surface measurement of mechanical specific energy (MSE) for enhancing drilling efficiency and maximizing rate of penetration (ROP). With an increase in directional drilling, surface measurement of MSE doesn't correlate with downhole conditions due to frictional losses along the drill string and bottom hole assembly (BHA). Attempts to measure weight on bit (WOB) and torque on bit (TOB) downhole typically involve expensive tools that are deployed 50-100 feet behind the bit. The authors present a novel in-bit strain sensor that measures WOB and TOB in the bit while drilling. This new approach is completely non-invasive and does not change the BHA. Field testing has shown that in many circumstances only 50% of the surface indicated weight reaches the bit and drilling performance suffers including low ROP and higher vibration levels. This highlights the importance of managing the directional plan and using methods to improve weight transfer. This new in-bit measurement tool has been used to drill over 100,000ft in applications around the world and provides useful insights into drilling performance.
On-the-Fly Proppant Flowback Control Additive
Authors: Zha Charles , Green John , Abrams Benjamin , Cabori Logan , Hamori Kevin , Harper Adam 
Published: October 2020

Abstract
This paper covers the research, development, and field application of a cost-effective, on-the-fly chemical additive that reduces proppant flowback during well production. Laboratory tests and field results, showing the effect this chemical additive has on proppant flowback, are examined.
Previous proppant flowback prevention methods are reviewed. Laboratory tests that were performed to ensure effective on-the-fly coating in the blender tub, as well as the ability to control proppant flowback, are also analyzed. A review of fracturing treatments performed in wells utilizing the proppant flowback control additive (PFCA) is provided. Post-fracture treatment proppant flowback and production results are compared to direct offset wells using conventional proppants. Fracturing treatment designs, formation characteristics, and proppant types are presented.
Laboratory proppant flow tests show PFCA-treated proppant can withstand a much higher critical flow rate than uncoated fracturing sand. Unconfined compressive strength tests indicate that proppant is coated efficiently in the blender tub, enabling effective consolidation at downhole conditions. The results of laboratory tests are used to explain and support the field case studies. The field case studies show that use of PFCA significantly reduces proppant flowback and typically results in higher hydrocarbon production when compared to conventional proppants used in offset wells.
With completion designs moving to finer mesh size proppant and higher proppant loadings per lateral foot, operators are increasing spending to deal with higher amounts of proppant flowback. This paper introduces an important technology that is an economically viable and beneficial substitute for traditional proppant flowback prevention methods.
A Machine Learning Approach to Reduce the Number of Simulations for Long-Term Well Control Optimization
Authors: Santos Daniel Rodrigues , Fioravanti André Ricardo , Santos Antonio Alberto , Schiozer Denis José 
Published: October 2020

Abstract
A long-term well control strategy is frequently selected using optimization methods applied to reservoir simulations. However, this approach usually requires a large number of simulations that can be computationally demanding. In this paper, we evaluated several machine learning (ML) techniques to reduce the number of simulations for optimizing long-term well control strategy while preserving the quality of the solution.
We proposed a methodology, denoted as IDLHC–ML, which combines many ML techniques with iterative discrete Latin hypercube (IDLHC) – a gradient-free optimization algorithm that was successfully applied in previous work – to optimize the coefficients of the logistic equation that guides the well's bottom-hole pressure along the time horizon. In IDLHC-ML, we used a set of simulation runs from the first iteration to train the initial ML models. From the second iteration onwards, we employed the trained ML models to predict the net present value (NPV) and only a percentage of the scenarios, which were expected to have the best NPV, were then simulated. As we simulated new scenarios, we updated our ML models to further improve predictions. For a fair comparison, we set the same values for the optimization parameters of IDLHC to the IDLHC–ML and, then, we compared the NPV and the number of simulation runs considering different configurations of IDLHC parameters. In this paper, we evaluated a total of twelve ML regression techniques, such as Bayesian Ridge, Random Forest, and stacked ensemble learning, which consists in using the predictions from multiple ML algorithms as input to a second-level learning model.
To minimize random effects, we repeatedly applied IDLHC and IDLHC–ML five times in a single reservoir model (nominal optimization). The results showed that, depending on the IDLHC optimization parameters, IDLHC-ML reduced at least 27% of simulations while keeping the equivalent NPV statistical metrics calculated in all five repetitions, when compared to IDLHC. Moreover, the best ML technique for IDLHC–ML varied with the IDLHC set of optimization parameters. To conclude, the method proposed here was able to reduce a significant amount of computational time by curtailing the total number of full-physics expensive reservoir simulations, with the help of fast and low-cost ML models.
There are many published studies in well control optimization, but these generally involve high computational demand. In this sense, ML methods revealed to be an adequate and inexpensive alternative in reducing the number of simulation runs in well control optimization. The methodology is generic and it can be applied under uncertainties, and for more complex cases.
A Paradigm Shift in Drilling Frac Plugs in Extended Laterals – A Permian Basin Case History
Authors: Krane Benjamin , DeFriend Matthew , Garza Heberto , Frantz Joseph , Tourigny Matt , Griffith Jared 
Published: October 2020

Abstract
Wells in the Permian Basin continue to increase in technical complexity and lateral length, testing the limits of traditional completion practices in unconventional wells. Coiled tubing has been the standard method for drilling out frac plugs in the basin but has mechanical limitations in extended-reach and high-pressure laterals. Due to the increasing well complexity operators have begun using high technology hydraulic completion units (HCU), also known as a snubbing unit, to drill out frac plugs in certain Permian Basin wells. By using the HCU in wells with longer laterals, higher pressures, and downhole uncertainty/complexity some of the additional risk can be mitigated. The HCU system can provide a reliable and cost-effective means to cleanout laterals in the Permian Basin while reducing mechanical risk. The purpose of this paper is to present case histories that illustrate the positive impact of the HCU on field operations. This paper will also detail the evolution of the HCU over time to its current state of the art stand-alone rigless system.
Transient Volume of Investigation: Definition, Theory, and Applications
Authors: Spivey J. P. , Lee W. J. , Sheikh Hafiz Mustafa 
Published: October 2020

Abstract
The transient volume of investigation extends the concepts of radius and depth of investigation (for radial and linear flow, respectively). However, it is far more general, with much broader applicability. Unlike the specific cases of radius and depth of investigation, the volume of investigation is independent of flow regime, geometry, and model. Thus, the transient volume of investigation is applicable even when no recognizable flow regime is present.
The transient volume of investigation is based on fundamental properties common to all constant-terminal-rate solutions to the diffusivity equation. It is defined as the minimum reservoir volume necessary to provide sufficient pressure support to produce the observed rate of change of sand-face pressure as a function of time.
The volume of investigation is a paradigm-changing concept. It is the only meaningful reservoir characteristic that is both easily estimated and model independent. It provides a rigorous lower bound on fluid in place investigated as a function of time during a test, independent of flow regime, geometry, model, and even permeability heterogeneity or anisotropy.
Reservoir Pressure Monitoring for Waterflooding Optimization using Downhole Dummy Pressure Sensors Installed in Injection Wells with Selective Strings
Authors: Tellez Camilo , Gonzalez Belo Patricio , Florez Fabian , Taborda Tatiana , Villa Carlos , Bonfanti Benjamin , Acosta Tito , Zuluaga Wendy , Parra Marly 
Published: October 2020

Abstract
Casabe is a multilayer field (20 reservoirs distributed in 3 formations) characterized high heterogeneous nature, limited sand continuity, unfavorable mobility, and high sediment production due to a poor consolidation of the reservoir. The ongoing waterflooding process has a well defined five-spot pattern where injection wells have selective strings installed, and production is commingled. Under these conditions, shut-in wells for reservoir pressure acquisition are challenging activities with high operational risks related to compromised mechanical integrity and sediment production. Likewise, the commingled production scheme affects the deconvolute pressure data process and promotes crossflow effects leading to inconsistent results. Finally, stoping the well involves production loss and disruption in waterflooding balance. The reservoir pressure is a critical requirement to run an analytic optimization methodology implemented in 2017. Casabe had proven that selective injection string offers a unique opportunity to acquire quality reservoir pressure data by reservoir unit using the Downhole Dummy Pressure Sensors (DDPS), overcoming all the above issues when acquired in producers.
This paper presents how a waterflooding optimization methodology coupled with a quality reservoir pressure acquisition enhanced reservoir surveillance by triggering actions to optimize the waterflooding volumetric balance, thus, reaching a production-injection system stabilization. This enhancement changed the area production decline trend and allowed to contact more zones and produce oil from constrained layers.
Deep Learning Based Hydraulic-mechanical Simulation of Petroleum Reservoirs
Authors: Wang Shihao , Liang Luming , Zhang Yanbin , Wen Xian-Huan , Wu Yu-Shu 
Published: October 2020

Abstract
Knowing that accurate yet time-efficient multihpysical reservoir simulation is a major challenge, we have developed a deep neural network (DNN) based multiphysical simulator to acceletate the geomechanical calculation. We first generated 20,000 pressure -stress pairs by conducting regious multiphysical simulation with various mechanical properties using a full physics reservoir simulator. All data pairs are simulated by 128 *128 grid blocks.
We then developed a U-Net network, a proven structure in image segmentation based on convolutional neural network (CNN), and trained the network with the generated data using Tensorflow. The U-Net network takes the pressure field as the input and regresses the stress field in a pixel-to-pixel manner. The model is trained with 100 epochs using Adam optimizer with a learning rate of 0.001 on GPU. The trained network is thereafter deployed in the reservoir simulator to predict the stress field from numerically solved pressure field at time step level. Hence the geomechanical simulation is replaced by the DNN proxy.
Our results show that the accuracy of the U-Net network is above 97%, in the metric of mean square error. Moreover, the U-Net network possesses excellent capability for dimension generalization. With the implementation of the proxy DNN simulator, the simulation time spent in the geomechanical module has been reduced by more than 95% on a 256*256 case study.
The novelty of our work lies in the pioneering application of DNN method to train and predict the time-dependent stress field, avoiding the curse of dimensionality. Our trained model can be conveniently connected with an existing reservoir simulator, which greatly saves the efforts of code development.