bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Swelling Materials to Control Fracture Complexity: Experimental Study
Authors: Gomaa Ahmed M. , Sun Hong , Nelson Scott , Qu Qi , Boncan Go Virgilio 
Published: September 2015

Abstract
Enhancing complexity of the created fracture geometry is the primary challenge for hydraulic fracturing treatment design in shale formations because of their stress anisotropy. Near-wellbore diversion may be required to evenly stimulate all perforated clusters; far-field diversion inside the created fracture may induce additional branch fracturing by overcoming the stresses holding the natural fractures closed. Increasing the pressure within the fracture enables further fracture extension.
The objective of this study is to experimentally confirm the bridging ability of a swelling diverter particle and to establish a correlation between increase in pressure and its location (far-field or near-wellbore) with time, pressure, proppant and diverter loading, fluid viscosity, and fluid types.
The study confirmed that increasing fluid viscosity and/or reducing flow permeability will increase the flow resistance (and therefore pressure) in any path. For fracturing application, an HEC fluid with extremely high viscosity (millions of centipoise) developed maximum increase in pressure of 3 psi/in. of fracture length. Swelling particles of the type described in the study can increase their diameter in water by up to 500% at 75°F and 800% at 175°F in water. If this growth occurs during flow in a fracture, it can preclude further movement and create a “bridge” that increases the fluid pressure inside the fracture. Injecting 100-mesh sand with the particles increased the bridging pressure from 12.5 psi/in. of fracture length for the particles alone to 125 psi/in of fracture length. The authors believe that this occurs because the sand particles create friction with the fracture face. Particles swell more rapidly in water than in 30 pptg linear guar gel, and more rapidly in linear gel than in crosslinked 30 pptg guar gel. Therefore, variations in the fracturing fluid can control the swelling of particles, a factor that can be used to “place” the diverter material deep in the fracture for far-field diversion, or closer to the perforations for near-wellbore diversion. An oxidizer breaker can be used to break the swollen particles, leaving no visible precipitation as indication of damage. The results from this paper can help in understanding the diversion parameters required to effectively enhance the complexity of the fracturing geometry.
Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells
Authors: Fulford David S. , Bowie Braden , Berry Michael E. , Bowen Bo , Turk Derrick W. 
Published: September 2015

Abstract
Production Data Analysis is a practice wrought with inconsistencies. In the application of any single model, the quantity of answers arrived at by experienced evaluators is often equal to the number of evaluators analyzing the data. The cause of such inconsistency is bias on the part of evaluators. While the colloquial use of bias typically implies systematic error, in this paper we define bias as an expression of belief by the evaluator. With the lack of recognition of bias, no manner exists in which to gauge its accuracy. A method that requires explicit expression of one's bias in rate-time decline behavior can provide an objective manner in which to evaluate it.
In this work, we present a machine learning method to forecast production in unconventional, liquids-rich shale and gas shale wells. Gong et al [2011] developed a method for probabilistic decline curve analysis using Markov-chain Monte Carlo simulation (MCMC) as a means to quantify reserves uncertainty, incorporate prior information (i.e. bias), and to do so quickly. However, their approach resulted in limited use of discrete P10, P50, & P90 production forecasts, as these often did not align with production data. We extend their approach by a) utilizing the Transient Hyperbolic Model (THM) to represent the various flow regimes present in unconventional wells, b) a methods for construction of representative "percentile neighborhood" forecasts, c) construction of Type Curves from an analyzed well set, and d) a modified likelihood algorithm to improve the accuracy of discrete forecasts. The accuracy and calibration of the method is demonstrated by an analysis of 136 wells in the Elm Coulee Field of the Bakken. Quantification of change in rate-time behavior due to completion design, and the inference physical behavior and properties, is demonstrated using a tight oil play in the Cleveland sand formation of the Anadarko Basin, and a shale play in the Wolfcamp formation of the Permian Basin.
We show that this implementation of supervised machine learning, in combination with well-calibrated bias, improves the estimation of uncertainty of the distribution of forecasts. Additionally, hindcasts performed at various time intervals results in accurate Mean 5 year cumulative production. We observe that the "percentile neighborhood" forecasts are reasonable fits of production data comparable to those that may be created by a human evaluator, and that the type curve computed is representative of the decline behavior of the wells upon which it is based. We conclude that, given the speed and accuracy of the process, machine learning is a reliable technology as defined by the SEC, and can replace the process of manual production forecasting by human evaluators for most unconventional wells with consistent trends of production history.
Secret Weapon Against the Red Queen: Using Chemical Packers and Degradable Mechanical Diverters in Refracturing Operations
Authors: Fragachan Franisco E. , Babey Andrew G. , Arnold David M. , Heminway Evan M. , Yuan Feng 
Published: September 2015

Abstract
Over the last five years, horizontal drilling, fracturing and multi-stage completion technology advances in unconventional shale resources has helped US oil production grow by 65 percent.1 Even though, production rates in unconventional plays can decline dramatically, up to 70% in the first year. To replace the loss in production, oil companies have traditionally drilled more wells. This decline phenomenon is called the Red Queen, named after the character in Lewis Carroll's “Through the Looking-Glass” who tells Alice “(Run) Faster! Faster! It takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”2 Refracturing may become the secret weapon against the Red Queen that allows existing shale wells to sustain hydrocarbon production without new wells being drilling.
In this paper, several case studies will highlight how temporary and degradable chemistry in both liquid and solid materials can be introduced as a means to provide temporary isolation between zones in shale refracturing operations. These chemical diverting agents forms a solid impermeable barrier or seal that in time will break or degrade to liquid form, leaving the existing zone that was diverted open to permit flow.
First scenario will focus on an open-hole sidetrack well drilled in an existing vertical well. Through the use of a chemical packer for support and isolation, a retrievable six zone sliding sleeve system was deployed. The second case and third case histories will focus on the impact degradable mechanical diverters are having not only on the refracturing market, but how they could potentially be used on new wells to increase zonal coverage and help mitigate the need for refracturing by increasing zonal coverage during the initial stimulation treatment.
This paper will present and discuss this unique application and performance of the fracturing completion design, and provide another approach of re-fracturing operation for unconventional plays.
Forecasting Production Performance in Reservoir-Network Coupled Systems with Asphaltene Modeling
Authors: Valbuena Ernesto , Barrufet Maria , Killough John 
Published: September 2015

Abstract
This manuscript presents the development and implementation of a new integrated reservoir-network compositional simulator for analysis of asphaltene precipitation within tubing and production pipelines. The need for integrated models for flow assurance analysis in assets with high potential of asphaltene precipitation is demonstrated for the following scenarios: i) natural depletion, ii) downhole asphaltene inhibitor injection, and iii) artificial gas-lift system.
Fully-implicit compositional formulation forms the basis for reservoir and network simulators. Implementation of a tightly-coupled solution allows assessing mutual interaction between subsurface and surface components. Compositional delumping from reservoir to network fluid characterizations enables accurate modeling of complex thermodynamic phase behavior for asphaltene precipitation. The Peng-Robinson equation of state with volume translation models phase equilibria in coupled solution and asphaltene precipitation in network pipelines. Asphaltene accretion to internal pipe walls is estimated with a mechanistic transport model to forecast reduction of flow area and its impact on production performance.
Solid precipitation and deposition in the production system have a negative impact on production rates, pressure management, and field operations. Results show increased thickness of asphaltene deposits in time, reaching up to 50% diameter reduction after a few months of production for the cases evaluated. Pipeline diameter increase at wellhead promoted asphaltene formation, caused by fluid velocity reduction. Bottomhole injection of asphaltene inhibitor reduced formation of solid deposits by 90%. Artificial gas-lift systems influence deposition profiles due to fluid compositional changes and velocity in the wellbore column.
Environmentally Preferred Process for Efficient Boron and Hardness Removal for Produced Water Reuse
Authors: Cao Jiasheng , Monroe Stephen , Hendry Dolly 
Published: September 2015

Abstract
Treatment and reuse of produced water has become increasingly attractive. The complicated nature of produced water, however, and its variability make it challenging to reuse. Boron (B), a common component in produced water, is a concern when reusing produced water for hydraulic fracturing fluids. Excess boron in water can interfere with the timing of crosslinking reactions in hydraulic fracturing fluids, and premature crosslinking can increase friction pressure (horse power) when the fluid is being pumped. Lowering the boron concentration in produced water, therefore, will help to prevent or reduce formation damage and increase the stability of the hydraulic fracturing fluid. This paper presents a patent-pending and environmentally preferred process for boron and hardness removal from produced water.
The traditional approach to boron removal has been to add chemicals (like activated alumina (Al2O3) or cerium oxide (CeO2)) or a chemically impregnated material (activated carbon) and clay as sorbents for boron removal from produced water, but these techniques have seen limited success. In these conventional approaches, sorption efficiency was affected by solution pH, initial boron concentration and other contaminants in the water. The method presented in this paper, however, is an environmentally preferred and simple process of adding liquid sodium silicate (Na2SiO3) to produced water for boron and hardness reduction. Efficient boron removal from more than 150 mg/L to less than 30 mg/L was achieved using this process, while at the same time, total hardness (as CaCO3) was reduced by more than 80%. While the waste solids that were created do contain high levels of calcium and magnesium silicate, it is proposed that they could be used for cementing material for oil industry applications thereby reducing the environmental impact of the process. Compared with other methods such as boron-selective resin, reverse osmosis (RO) or chemical sorption, this process exhibited better performance in the efficient removal of boron and hardness from produced water.
Geology Driven EUR Prediction Using Deep Learning
Authors: Crnkovic-Friis L. , Erlandson M. 
Published: September 2015

Abstract
We present a geology driven deep learning Estimated Ultimate Recovery (EUR) prediction model for multi-stage hydraulically fractured horizontal wells in tight gas and oil reservoirs. The novel approach was made possible by recent development in the field of deep learning and the use of big data (200,000+ geological data points and 800+ wells). A Deep Neural Network (DNN) was trained to learn the relationship between geology and the average EUR (estimated by decline analysis). The model was validated on wells from other geological regions to show its generalization capabilities.
The DNN model we present significantly outperforms both volumetric estimates and type curve region averages (even on highly developed acreage). It generalizes well across geological areas with limited loss in accuracy. On a test region not used during model creation it produces a mean absolute percentage error of 33.1% compared to 69.7% for type curve averages. Oil and gas recovery are treated separately and the model outputs the oil to gas ratio. The model was trained and tested on data from the Eagle Ford Shale but the general methodology should be applicable to other resource plays.
The model is applicable in the exploration stage, as it only requires geological data. This is important as type curve regions require production data to be constructed, and are thus not available until the area has been in production for some time.
Evaluation of the EIF Onshore Discharges Tool for Assessing Environmental Risk from Accidental Spills and Leaks Associated with Hydraulic Fracturing Operations
Authors: Firth S. K. , Musdalslien U. I. , Hagemann R. E. 
Published: September 2015

Abstract
Prompted by regulatory requirements to work towards “Zero harmful discharges” to the Norwegian Sector of the North Sea, the oil industry developed the “Environmental Impact Factor (EIF) Produced Water” risk management tool. This helps to identify best measures for reducing risk from the offshore discharge of produced water and is used by all operators in the Norwegian Continental Shelf for reporting continuous improvement. Its success has led to the application of the EIF concept as part of the risk evaluation for other types of discharge, both onshore and offshore. This paper presents the EIF Onshore Discharges tool and evaluates its use for application to hydraulic fracturing (HF) operations.
The EIF Onshore Discharges tool uses quantitative modelling techniques in combination with generic principles for environmental risk assessment to predict the volume of three receptor compartments (soil, groundwater and surface water) potentially impacted as a result of intentional or unintentional liquid discharges or releases. In this instance the tool has been used to predict potential impacts from a range of accidental release scenarios associated with hydraulic fracturing (HF) operations. These included surface and subsurface spills/leaks of HF fluids and flowback water. Various HF fluid blends were assessed using a variety of release locations to represent the range of key factors affecting risk. The purpose has been to raise awareness and identify proper risk mitigation measures.
The results indicated a large range in potential impacts arising from unmitigated accidental releases. This variation could be explained by differences in the release scenarios, HF fluid composition and variability in receiving environment conditions. The results also appear to be in line with impacts experienced from known spills, adding confidence to the tool's predictive ability.
Whilst the probability of such releases occurring is very low, the fact that some scenario/chemical combinations have the potential to cause significantly higher impacts than others helps identify key considerations for risk management decisions. In particular, the modelling demonstrated that some HF fluid blends posed significantly higher environmental risk than others, prompting consideration of chemical substitution as part of the overall risk management strategy for the HF operations. Other ways in which the tool aids risk management decisions include identifying higher risk locations where additional risk prevention and/or mitigation measures would be warranted and helping to define spill response measures.
End-to-End Data Provenance
Authors: Rundle R. T. , Vuurboom R. , Duroy Y. 
Published: September 2015

Abstract
Data provenance is the chronology of an object, i.e., all the transformations and actions that modified the state of the object from the time of creation to the present. End-to-end data provenance is the ability to account not only for the data provenance of a single object, but also to account recursively for the data provenance of all other objects that affected the state of the object. The purpose of data provenance is to help ensure data integrity.
Modeling has become an important aspect of shared earth problems. As the role and extent of modeling of the subsurface expands and its use is taken up by larger teams, determining the provenance of data becomes ever more important. The explosion of data and the appearance of increasingly diverse teams require the establishment of better data provenance to guard the integrity of the modeling results. Such a data provenance model is presented in this paper.
The economic consequences of such an approach are significant: better integrity of modeling results leads to more confidence in those results, which reduces perceived risk and, in turn, can improve the economic performance of field development.
The solution to the problem of end-to-end data provenance has six elements: The ability to uniquely identify objects of the shared earth problemAn object repository that supports federation and extensibilityThe ability to associate an immutable change history with each objectThe loose coupling of metadata and actual data of an objectClient-side caching of actual dataA comprehensive security model for the object.
Each of these elements solves an important aspect of ensuring data integrity. Such an approach is increasingly important as model-based approaches become ubiquitous, and the available data become more voluminous, and the teams that handle the data and modeling results become larger and more diverse.
Root Cause Failure Analysis as a Tool for Investigating Operational Failures: A Case Study
Authors: Scolnick Spencer E. , Garrett Jonathan C. , Griffith Steven L. , Ward Kevin K. 
Published: September 2015

Abstract
Production liner cementation in deepwater wells is critical to contain formation pressures and provide zonal isolation. This is especially challenging in the Gulf of Mexico because of depleted zones, small drilling margins, and HPHT reservoirs. Failure to achieve zonal isolation with the primary cement job leads to added cost and time for remedial operations. A recent primary cement job failure in the Viosca Knoll Block, located in the Gulf of Mexico, prompted the use of an established tool, Root Cause Failure Analysis (RCFA), in a novel way to identify the causes of the incident and avoid repeating the same mistakes in future wells.
A team of operations and engineering personnel was assembled to investigate the cause of the primary cement job failure that resulted in nonproductive time and cost of approximately 7 days and $3 MM, respectively. RCFAs have typically been performed on equipment failures; however, this RCFA was conducted by the operator on a non-equipment related failure.
The RCFA team initially collected data available from both the planning and operation phases of the job in order to identify which elements of the cementing process contributed to the cement job failure. After identifying the root cause, the team communicated learnings to office and rig-based personnel, developed a new tool for real time monitoring of primary cementation, and adjusted plans for upcoming primary cement jobs. The findings from the RCFA were applied to a well on another asset owned by the operator, with which the production liner was successfully rotated throughout cementation for the first time.
The RCFA identified that the cement job design was not a contributing factor; instead, the root causes were operational and started during circulation of drilling fluid prior to cementing operations. The learnings from the RCFA were transferred to both office and rig-based personnel, influencing engineering and operations plans for subsequent wells. Furthermore, the investigation identified previously unknown issues and showed that the use of the RCFA process was an effective approach that can be standardized and scaled to determine root causes of future operational failures.
Predicting and Optimizing ROP in Competent Shale by Utilizing MPD Technology
Authors: Mammadov Elvin , Osayande Nadine , Breuer John , Al-Hashmy Wamidh 
Published: September 2015

Abstract
Advanced Managed Pressure Drilling (MPD) technology was used onshore in Western Canada to optimize ROP while drilling horizontally through the potentially sour shale formation using a low solids, low density synthetic base mud system. The Montney shale formation is very challenging to drill conventionally and has the potential for drilling into high pressure fractures which may contain sour gas. The overall objective consisted of using a lower density synthetic base mud system to optimize the ROP, thereby saving on overall drilling costs by reducing the number of drilling days and also drill the well safely due to the possibility of high pressure fractures and potentially sour gas. The main challenge was to increase ROP while still maintaining a constant bottomhole pressure to prevent any gas inflow into the wellbore.
In order to optimize drilling efficiencies and save on drilling time, advanced MPD technology was applied in combination with a low density (770 kg/m3) synthetic based mud while holding surface back pressure (SBP) in dynamic and static conditions to maintain a constant bottomhole equivalent circulating density (ECD). The system's annular pressure control mode was used to hold a desired ECD at a pre-determined position within the wellbore; In this case the heel was chosen. Constant bottomhole pressure operations were maintained throughout the entire project. The desired ECD was achieved and the well was kept at balance with the pore pressure. The advanced MPD system also provided the ability to monitor, detect and control an influx at the bit much earlier than conventional MPD and drilling systems which provided additional safety to the location. While drilling conventionally the ROP was extremely slow averaging 3.32 m/hr. The results greatly improved using advanced MPD technology with average ROP's between 7.4–10.5 m/hr.
This application also brought value to the project by reducing mud costs, increasing overall ROP as compared to conventional offset ROP data, maintaining constant bottomhole pressure with flexibility to adjust pressures as the well dictated, increasing safety through detection and control of micro influxes while drilling, reducing bits used as compared to conventional offset bit record data and minimizing any gas to surface. Achieving the goal of increasing the ROP and safely drilling the well to TD reduced the number of drilling days by at least 13 days, thereby reducing the overall AFE of the well.
This paper will include engineering analysis of the project compared to offset data along with graphs and tables. It will further include detailed explanation of pre-engineering and execution methods of advanced MPD in order to achieve ROP optimization and reduce your drilling cost.