bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantifying the Probability of Success of Stimulation Treatments When Information is Limited
Authors: Hoeink Tobias , Cotrell David , Odusina Elijah , Ghorpade Sachin 
Published: October 2018

Abstract
A paradigm shift in dealing with subsurface uncertainty in hydraulic fracturing treatments is introduced. The mathematically rigorous application of uncertainty and sensitivity analyses for a proposed stimulation of a lateral well within an unconventional reservoir in the Marcellus with limited formation data delivers the ability to identify the optimum treatment parameters and to quantify its probability of success. Selection of the optimum reservoir stimulation treatment is achieved by systematically investigating thousands of hydraulic fracture simulations over a large parameter space covering formation properties with inherent uncertainties (e.g., stress gradients, leak-off coefficients) and tunable treatment parameters (e.g. pumping rates, fluid and proppant properties, perforation spacing), and computing an objective function. Operators commonly select objectives based on technical (e.g., propped fracture length, fracture height containment), operational and investment considerations. Here, the average fracture conductivity at closure is selected as the primary technical objective to be maximized. A subsequent uncertainty analysis of the optimum treatment plan that expressly includes the limits of formation property knowledge quantifies the probability of success. Production forecasts of specific cases illustrate the range of possible outcomes. Results from more than 12,000 hydraulic stimulation simulations demonstrate a wide distribution of results in terms of average fracture conductivity. Surprisingly, only a small, isolated fraction (&lt; 5%) of the design space returns clearly superior results compared to the majority of investigated scenarios. The optimum treatment designs in this study are associated with relatively low volumes of a gel treatment pumped at relatively high rates. Production simulations illustrate that the best 10% of cases significantly outperform production over the first two years by approximately 50%. Collectively, the approach presented here illustrates the application of uncertainty and sensitivity analyses on several thousand simulations that cover a large, realistic parameter space. Embracing uncertainty, this approach enables identification of the best treatment plan and quantification of the probability of success given limited formation data. In addition, this methodology offers input for risk assessment and return on investment decisions.
AI Supports Information Discovery and Analysis in an SPE Research Portal
Authors: Schoen Eric , Smith Reid , Boden John 
Published: October 2018

Abstract
Ongoing growth in the volume of raw data generated by digitized oil and gas operations has been widely documented (Spath, 2014). What may be less apparent is that the industry is also authoring dramatically more unstructured content—interpretations, learnings, case studies, etc.—on an annual basis. This is not surprising, as anecdotal evidence suggests that most decisions are taken on the basis of unstructured data (Quaadgras & Beath, 2011) (Garland, 2017) (Palkowsky, 2005) (Haines, Shaughnessy, & Briggs, 2006) (Hollingsworth & Schey, 2017). At the Society of Petroleum Engineers, the number of new papers published annually has followed an exponential growth curve that doubles approximately every 10-11 years, beginning as far back as the early 1950's.
Mapping the Success Dimensions of Large Oil and Gas Projects
Authors: Redda Yitbarek , Turner Rodney 
Published: October 2018

Abstract
In order to satisfy the increase in world energy demand, the global oil and gas industry is expected to invest US$ 25 trillion from 2015 to 2040 in development projects (IEA, 2015). However oil and gas projects have had a long track record of sluggish performance. This study investigated the performance of Development (Pre-FID) Large Oil and Gas Projects and determinants of their success. Adopting a view that project success comprises success factors and criteria considered simultaneously, 11 success criteria and 57 success factors were first identified using a systematic and exhaustive literature review backed up by validation from experienced practitioners. A global online survey was then conducted to solicit the perceptions of project directors and managers from Owners and EPC Contractors regarding the identified success criteria and factors. A total of 96 and valid responses were received.
Exploratory factor analysis using SPSS software identified three success criteria groupings - (1) Project Management Success, (2) Business Success, and (3) Future Potential/Growth. For success factors the number of groupings is six. These are (1) Project Management, Leadership & Team Competence, (2) Front End Loading, (3) Project External Context and Compliance, (4) Project Impacts on External Environment, (5) Project Risk and Quality Management, and (6) Project Connectivity with Local Resources Capacity. The criteria and factors identified are directly applicable to projects and programs in other domains such as mining, large scale infrastructure development, ship building, and defense acquisition. The results of this study can be used as a starting point towards the development of a success measurement methodology for industrial megaprojects.
Gas Velocity-Based Lift Curves for Quantifying Lost Production in Liquid-Rich Permian and Delaware Basin Horizontal Liquids Loaded Wells
Authors: Nagoo A. S. 
Published: October 2018

Abstract
In this continuation work, we expand upon the Nagoo et al., SPE-190921 [1] seminal paper that unveiled for the first time a simple and direct analytical critical gas velocity diameter-and-inclination-dependent equation for predicting the onset of liquids loading in horizontal wellbores. Using this equation, we now introduce a new analytical method for quantifying lost liquids production in liquids-rich horizontal gassy oil and gas wells undergoing liquids loading. Case studies of in-operation horizontal wells from the Permian and Delaware basins are used to highlight and validate the methodology.
For horizontal liquid-rich wells in unconventional plays, liquids loading can severely impair production in gassy oil or gas wells with significant oil or water production. Currently, there is no reliable, field-focused, simple-to-use, analytical method for quantifying the lost (or deferred) liquids production because of the liquids loading happening in the wellbore. We propose that the downhole wellbore gas velocity and the critical gas velocity profiles can be superimposed on the traditional wellbore lift curve to yield a varying unloading point on the wellbore lift curve that is very sensitive to and dependent on the predictive reliability of both the diameter-and-inclination-dependent critical gas velocity model used and the wellbore multiphase flow model used. In looking at the new gas velocity-based lift curve results for several horizontal wellbores, the lost liquids production can now be quantified and compared to the actual liquids production drop in the well history before and after the onset of liquids loading. Furthermore, it is demonstrated that as opposed to prior ad hoc recommendations of bottom-of-lift-curve or tangent-of-lift-curve demarcation points for liquids unloading, the new analysis presented provides an analytical intersection point between the downhole wellbore gas and critical gas lift curves as the basis for the unloading point (onset of flow liquids flow reversal point). This signifies that the combination of analytical multiphase flow wellbore and analytical critical gas velocity calculations will now change and define the range of unloading producing rates according to diameter, inclination and fluid property changes.
For the first time, a practically useful and simple analytical method for quantifying lost liquids production in liquids loaded horizontal gassy oil and gas wells is presented in the form of gas velocity-based lift curves. This signifies a new powerful arm of horizontal well artificial lift modeling for life-of-well lift optimization (i.e., incorporating rate declines) for keeping wells flowing at flow rates to avoid liquids loading. Both the lost liquids and lost gas production can be predicted on a well-by-well basis and wells can now be prioritized for artificial lift needs according to their downhole "loading proximity". Various relevant field case studies are used to validate the method in practice.
Comparing Completion Designs Using Normalized Rate Transient Analysis – Proposed Workflow and Case Study
Authors: Ockree Matthew , Brown Kenneth G. , Frantz Joseph H. , Voller Katharine , He Jonathan 
Published: October 2018

Abstract
Proper design and analysis of field tests requires two critical components: 1) availability of quality production data from appropriate control wells, and 2) a normalization process capable of isolating the impact of well and/or completion design changes on well performance.
We will present a normalization workflow currently employed in our Marcellus Shale assets. Normalization methodologies published by others will be summarized and discussed in this paper as well. Lastly, we will compare these published methodologies to our proposed approach, noting the pros and cons of the various approaches.
We begin by discussing an updated rate transient analysis (RTA) workflow (update of SPE-165711) that is based on analysis of 1,000+ Marcellus Shale wells with several years of production history, and incorporates several "lessons learned" in regard to non-uniqueness issues and input uncertainties. Details of our proposed normalization process will then be presented, which involves Production Data Analysis (PDA), Rate Transient Analysis (RTA), and analytical modeling. Lastly, we will explore a case history illustrating the value of this normalization process.
The case history will demonstrate the use of this normalization process to assess the value of different completion designs. This study will clearly illustrate the value of implementing an adequate normalization process to 1) assess field tests, 2) accurately forecast well productivity, 3) evaluate well economics associated with further testing, and 4) better understand why a new design performs differently from previous designs.
Statistical Analysis of Generational Effect on Marcellus Well Completions Using Bottomhole Gauge Data
Authors: Ayers Kimberly , Jacot Henry , Ayers Alivia 
Published: October 2018

Abstract
In the Appalachian Basin, the primary focus has shifted from exploratory wells to full pad development. Therefore, generational affects--the relationship between parent and child wells--are becoming a primary concern to operators that are fully developing their lease positions. This study examined bottomhole gauge data from a parent Marcellus Shale well that was recorded during the hydraulic fracture stimulation of three children wells in the Marcellus Shale and two children wells in the Burket Shale.
The characteristics of the children wells created a robust data set due to variation in inter-well spacing, producing formation, completion design, and stimulation timing. Fracture modeling was performed in advance of the completion operations in order to mitigate possible parent-child communication. The parent well produced natural gas and condensate for nine months prior to being shut-in for the completion of the children wells. Rate transient analysis was performed on the parent well to further understand the depletion of the producing zone. Detailed bottomhole pressure and temperature data were measured in the parent well during the stimulation.
Once operations were completed, the bottomhole gauge data was examined to identify frac hits to the parent well. The general magnitude and timing of the frac hits were examined in relation to the rock matrix and completion design parameters, and completion sequence. It was concluded that over 75 percent of Marcellus frac stages communicated with the parent well, with the most frac hits being attributed to the nearest child well. Logistic regression tests were performed on individual parameters to determine key influencers on the likelihood of a frac hit.
Detailed bottomhole gauge data, as presented in this paper, is limited in the current literature due to the expense of data acquisition. The unique characteristics of the wells involved in this field experiment provide for robust statistical analysis that is not typically available publicly in the Appalachian Basin.
Mining FracFocus and Production Data for Efficacy of Fracturing Fluid Formulations
Authors: Luo Hongyu , Zhang Yongqiang 
Published: October 2018

Abstract
Using environmentally friendly and low-cost chemical ingredients that are also effective in terms of oil and/or gas production is the ultimate goal of formulating fracturing fluids. The relatively transparent production data and fracturing fluid ingredients through FracFocus provide an opportunity to mine large amount of data for insights on the efficacy of fracturing fluid ingredients, which were not possible through traditional means of individual lab tests and statistical analysis of individual ingredients and small clusters of wells.
Here we showcase a big data analytics framework where through data preprocessing and feature engineering, algorithms and models can be built to quantify the efficacy factors of fracturing fluid ingredients for gas production. Special attention is paid to geological properties so that they are similar or identical for comparison.
As an example, we demonstrate the big data analytics process on a dataset of more than 3100 horizontal Marcellus shale gas wells in Pennsylvania, and the data-based insights not only corroborate the empirical wisdoms known in the industry, but also established quantitative measures such that ingredients with higher efficacy factors should be used more confidently, while caution should be exercised on those ingredients that have negative efficacy factors for gas production.
This work also shows the potential value of the FracFocus database in improving the efficiency and efficacy of hydraulic fracturing, in addition to its intended environmental cause. Big data and data sharing should be encouraged not only for public safety grounds, but also for economic reasons for the industry.
Accounting for Serial Autocorrelation in Decline Curve Analysis of Marcellus Shale Gas Wells
Authors: Morgan Eugene 
Published: October 2018

Abstract
Current decline models fail to capture all of the behavior in shale gas production histories. That is, upon fitting one of these models, one often sees significant and sustained deviation of the flow rate data points from the decline trend. One way to measure this "lost signal" is to look at the autocorrelation in the residuals about the fitted decline model. Indeed, with many shale gas wells we see significant amounts of autocorrelation, especially when comparing the flow rate at one time to the next (lag one). Theoretically, this serially autocorrelated error can impact decline curve analysis in two ways: 1) inefficient estimation of decline curve parameters, and 2) lost signal in the data. Borrowing from time series statistics, there are two conventional ways of dealing with these potential problems: 1) estimate the decline curve parameters with generalized least squares or generalized nonlinear least squares, and 2) fitting an ARMA model to the residuals and adding it to the fitted decline curve.
This paper investigates the practical implications of these two procedures by exercising them over decline curves fit to 8,527 Marcellus shale gas wells (all wells from that play with viable data for the analysis). The study explores the effect that generalized regression methods and ARMA-modeled residuals have on six different decline curves, and performance is measured in terms of sum of squared residuals (a metric for goodness-of-fit, calculated on the training data (first 24 months of each record)) and mean absolute percent error (a standard metric for forecasting accuracy, calculated on the testing data (all production rates after 24 months)).
We find that inclusion of the ARMA-modeled residuals largely improves the goodness-of-fit for any decline curve, and improves the forecasting accuracy for the Hyperbolic decline curve and Duong's model. The use of generalized least squares or generalized nonlinear least squares has little benefit in fitting the decline curves, except for the Logistic Growth model, where it improves both fit and forecasting accuracy.
Adopting Physical Models in Real-Time Drilling Application: Wellbore Hydraulics
Authors: Shahri Mojtaba , Kutlu Bahri , Thetford Taylor , Nelson Brian , Wilson Timothy , Behounek Michael , Ambrus Adrian , Ashok Pradeepkumar 
Published: October 2018

Abstract
The first step towards drilling optimization and automation is a reliable data acquisition and handling system. This includes receiving and processing different frequency data across multiple platforms and ensuring proper data quality. Once we implement such a platform, we can build different advisory solutions to improve drilling efficiency and move towards drilling automation. The developed Drilling Intelligence Guide (DIG) already enabled us to achieve the aforementioned goal and access different data (from contextual to high frequency) in real-time.
In the next phase, different models (physical or data analytics) can be built in to optimize various stages of drilling operations. The drilling industry has made significant progress on different physical models that can be run offline (either for pre-job design or post-job analysis) in the previous decades. Given the developed data platform, we can now adopt and run these models in real-time for optimization and automation purposes. As an example, a real-time, computationally efficient hydraulic model that can account for drill string rotation and eccentricity would enable us to monitor ECD (to be used for wellbore stability, kick and lost circulation mitigation) and calculate pump pressure corresponding to different operational conditions (to be used for drill string washout/pump failure prediction and sensor calibration).
The aim of this paper is to show the application of the hydraulic model for real-time monitoring and optimization purposes. An analytical hydraulic model including drill pipe rotation and eccentricity effects is used and compared against transient numerical simulations. In the next step, Pressure While Drilling (PWD) data are used to verify the accuracy of the model for real-time applications. After the verification steps, we explain the process to couple real-time data (e.g., flow rate, RPM), contextual data (e.g., mud properties, BHA and wellbore geometry) and the physical model using field examples. In addition to ECD monitoring at the bit and casing shoe, the real-time hydraulic model can also be used to monitor washout and pump failure events. The application of the model is shown using field examples.
The Impact of the Net Stress on Gas Recovery from the Marcellus Shale
Authors: El Sgher Mohamed , Aminian Kashy , Ameri Samuel 
Published: October 2018

Abstract
Unconventional reservoirs have high initial production rates followed by a steep decline as compared to conventional reservoirs. The increase in the net stress with the production results in matrix and fissure permeability reduction and hydraulic fracture compaction and conductivity impairment due to proppant embedment. At the same time, the pressure decline will result in gas slippage and matrix permeability enhancement. The impact of the net stress and pore pressure changes are often neglected when evaluating the production performance of the shale wells. The objectives of this study are to investigate the impacts of net stress changes (geomechanical) and pore pressure changes (gas slippage) on the gas production from horizontal wells with multiple hydraulic fractures completed in the Marcellus Shale. Laboratory measurements on Marcellus shale core plugs provided the foundation for evaluating the impact of pore pressure and net stress changes on the matrix permeability. Additionally, these laboratory measurements on Marcellus shale core plugs provided the fissure closure stress. The results of the published studies on Marcellus shale core plugs were also utilized to develop relationships for hydraulic fracture conductivity and the fissure permeability as a function of the net stress in the shale. Core, log, completion, stimulation, and production data from the wells located at the Marcellus Shale Energy and Environment Laboratory (MSEEL) were utilized to generate the formation and completion properties for the base model for a horizontal well completed in Marcellus Shale. The results of the laboratory measurements and published studies were then incorporated into the base model to account for the impact of the stress on the matrix, fissure, and hydraulic fracture permeability (conductivity), and consequently on the production performance.
The model was utilized to determine the effective properties of the hydraulic fractures by history matching the production data from two horizontal wells at MSEEL site. For the comparison purposes, the geomechanical effects were excluded from the model, individually and all combined, to history match the same production data from the horizontal wells. The results indicated that the geomechanical effects for fissure permeability have a significant impact on gas production as compared to geomechanical effect for matrix permeability and hydraulic fracture conductivity. The gas slippage was found to have an insignificant impact on the production. The base model was finally used to perform a number of parametric studies to investigate the impact of fracture half-length, initial fracture conductivity, and fracture stages spacing on the stress-dependent fissure permeability.