bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Pilot-Scale Evaluation of Natural Gas-Based Foam at Elevated Pressure and Temperature Conditions
Authors: Beck Griffin , Bhagwat Swanand , Day Carolyn , Gordon Emilio , Daeffler Chris , Malpani Raj , Verma Sandeep , Chaves Leo , Comeaux Bruce , Chrusch Larry , Naik Sarvesh , Renk Joseph 
Published: October 2020

Abstract
Nitrogen (N2) and Carbon Dioxide (CO2) foams have been used as hydraulic fracturing fluids for several decades to reduce water usage and minimize damage in water-sensitive reservoirs. These foam treatments require gases to be liquefied and transported to site. An alternative approach would be to use natural gas (NG) that is readily available from nearby wells, pipelines, and processing facilities as the internal, gaseous phase to create a NG-based foam. Hydraulic fracturing with NG foam is a relatively inexpensive option, makes use of an abundant and often wasted resource, and may even provide production benefits in certain reservoirs. As part of an ongoing development project sponsored by the Department of Energy (DOE), the surface process to create NG foam is being developed and the properties of NG foam are being explored. This paper presents recent results from a rigorous pilot-scale demonstration of NG foam over a range of operating scenarios relevant to surface and bottomhole conditions with a variety of base-fluid mixtures.
The Pilot-scale Foam Test Facility (PFTF) used in these investigations is first described. The PFTF is capable of generating foamed fluids at pressures up to 7,500 psig and at temperatures in excess of 300°F. Then, results from several investigations aimed at proving NG foam at conditions relevant to the field are presented. NG foam was characterized using rheology measurements and flow visualization techniques. Experiments were performed to investigate the texture and stability of NG foam generated by two different mixing methods: one using a custom designed tee to match mixing velocities in the field where the gas phase is jetted into the aqueous stream, and another to ensure comprehensive mixing for laboratory analysis. Parametric studies were conducted to explore the effects of flow rate, foam quality, and temperature on the stability of NG foam. Moreover, different fluid preparations were used to investigate the effect of base fluid and additive concentrations on the stability of NG foams. Additional laboratory studies that investigated foam stability with produced water and multicomponent NG mixtures are also reported.
The NG foams explored in these investigations exhibited typical, shear-thinning behavior observed in rheological studies of N2- and CO2-based foams. The measured viscosity and observed stability indicate that NG foams are well suited for fracturing applications. Like other foams, NG foam exhibits sensitivity to operating temperature characterized by a decrease in apparent viscosity as temperature increases. Rapid foam breakdown was observed at significantly elevated temperatures exceeding 290°F. In addition to fluid characterization, these investigations also yielded several key lessons that should be applied to future field demonstrations of NG foam.
Development of an Integrated Well Planning Platform in a NOC
Authors: Esteves Aranha Pedro , Abensur Gandelman Roni , Henrique Martins dos Santos Pedro , Borella Hougaz Augusto , Gomes Clemente Ricardo , Dumlao Vincent , Henrique Santos Teixeira Pedro , Mazzi Vitor 
Published: October 2020

Abstract
The act of planning a well is a collaborative effort taking disparate data from various groups and synthesizing it into one overarching program. Each operator follows its own well design process, but common to each operator is the fact that wells not designed wholly in series, but rather have many parallel sections, with a set of decision gates and countless data interdependencies. The goal of this paper is to detail the successful development and implementation of an integrated planning platform within a National Oil Company.
The individual components of well design process are not performed in a vacuum. The various inputs / outputs from one application affect the inputs/ outputs of applications both upstream and downstream of it, along the design process. This is addressed by facilitating integration and analysis of data input / output from each component of the planning process, synthesizing it, and performing automated system-integrity and overall conformity checks between the interdependent components. Any changes to an individual design component that affect other areas must be flagged, notifying the appropriate parties. Defining and automating workflows, mapping data interdependencies within the workflows, and creating a system of data governance are also keys to building an efficient planning platform. Similar to a navigation program which is able to determine an optimal driving route by considering various dynamic and static data points, the implemented platform uses a data-adaptive approach to well planning. Using this method, it takes unstructured data processes, decision trees, data integration protocols, and automates them while also permitting users to collaborate on well planning and design, allowing the standardization of processes. Operators are able to embed their particular methods of well design into the software platform and thus ensure that all of their company meet the specific requirements. Key to the effectiveness and longevity of this type of platform is a neutral data repository. Allowing data to be free of any singular data protocol ensures that although individual applications may revised, replaced, or put into competition with one another, the format of the data produced as well as ingested by these applications stays constant. Data will remain mapped, both in terms of overall process workflow and interdependencies. This paper is beneficial to any operator wishing to gain insight in developing a forward thinking of a digital strategy for well planning and design. However, the use of these types of methods and development of a similarly integrated platform an operator can standardize processes, enforce and ensure data governance, as well as gain efficiency in overall planning time.
Integrated Fracture Characterization and Flow Profiling with Distributed Temperature Sensing
Authors: Sun He , Yu Wei , Sepehrnoori Kamy 
Published: October 2020

Abstract
Distributed temperature sensing (DTS) is an enabling technology for fracture diagnosis and multiphase flow measurement in unconventional areas. DTS data analysis includes the warm-back stage and production stage analysis. The warm-back stage analysis can provide the slurry flow and proppant allocation. The production stage analysis can be applied to flow profiling and fracture characterization. The objective of our DTS data analysis approach is to provide an integrated quantitative diagnosis of effectiveness of staged fracturing, and hydraulic and natural fractures with the full-physics model, which will benefit the fracturing operation design and decision-making process in the unconventional reservoir. In this work, we developed a comprehensive numerical forward model for DTS data analysis. Our model includes reservoir and wellbore models. Also, the flow and thermal models are fully coupled. A thermal embedded discrete fracture model (Thermal EDFM) is developed to handle the thermal modeling of complex fracture networks. The DTS analysis with our model provides a high-resolution solution since the fracture diagnosis and flow profiling are performed for each fracture. With this analysis, we obtain a deeper understanding of the effectiveness of the field hydraulic fracturing operation. Although numerous simulators are developed for DTS data analysis, relatively few existing models can handle the full-physics such as complex fracture geometry and multiphase flow. Our inverse model provides an improved DTS data match result. Our model is more rigorous than the prior models to simulate and match the field DTS data.
Quantifying Resilient E&P Portfolio Strategies
Authors: Allan Paul , Amadi Ugo 
Published: October 2020

Abstract
E&P companies frequently mention portfolio resilience when describing their strategic plans or the composition of their opportunity inventories. As a general concept, the ability to recover from adversity quickly and deliver performance objectives under multiple scenarios is a critical characteristic in any strategic plan. While there is little disagreement in the value of ‘resilience’ as a portfolio characteristic, there is significant variance in the ways this attribute is described or measured as related to E&P portfolios. In many cases, available planning software or valuation tools drive the ways that resilience is considered, resulting in measures that may or may not actually reflect the ability to adapt and still deliver desired performance objectives. This paper provides a structured and repeatable method for quantifying portfolio resilience, with examples provided to clarify the value in applying these techniques.
To demonstrate the methodology, a simple portfolio model reflecting a typical E&P asset portfolio was developed. Resilience as measured using this approach is the ability for a given strategy to deliver set performance expectations when exposed to a broad range of uncertainties or adverse scenarios (above and below ground). These adverse scenarios or ‘torpedoes’ are applied to the portfolio and the cumulative performance difference is computed across multiple objectives and used as a measure of aggregate downside impact. Measuring ‘resilience’ as the ability of a portfolio to recover from selected ‘torpedoes’ in delivering performance is quantified, and portfolio selections are then optimized using linear programming to maximize the resilience measure. The resulting performance of this portfolio is then compared to other optimized portfolios to quantify the trade-offs between performance, resilience, value, and risk. The examples provided clearly depict the value in quantifying a resilience measure and introducing this into the decision-making process. Some of the alternative methods of assessing resilience will also be assessed in this same frame, allowing a direct comparison of these techniques.
While the concepts and techniques outlined in this paper are not entirely new, the combination of aggregate variance computation, linear programming, and rapid assessment of alternative scenarios (torpedoes) provides a practical and replicable measure of E&P portfolio resilience. As objectives and key performance measures are typically fluid, these methods may be adapted quickly for real-time insights into portfolio decisions and value versus risk trade-offs between competing strategic plans.
Improved Stacked Permian Development by Integrating DNA Diagnostics with Traditional Reservoir Analysis
Authors: Schlecht Mathias , Sawadogo Jordan , Sadeghi Simin , Reeve Nico , Haggerty Matthew , Liu Joanne , Ursell Luke 
Published: October 2020

Abstract
Permian operators have dramatically increased the number of multi-stage fractured horizontal wells over the past 5 years and face challenges associated with maximizing production of existing wells while developing new acreage and benches, all the while meeting capital return requirements. Over that time, DNA diagnostics have been applied successfully to more than 1000 wells throughout the Permian Basin to help operators reduce uncertainties ranging from drained rock volume, well-well communication, and sources of water production.
When subsurface conditions change, microbes change, and the DNA from microbes can be used to profile total fluid flow (water + oil phases) from benches and between wells. It therefore serves as a powerful tool to provide a range of answers, using advanced analytics and integration with various data sets. In this study, we will provide the background of DNA diagnostics and related analytics, along with the latest insights into viable operating environments. We also highlight recent Permian basin projects that have used DNA in conjunction with operator data to reduce uncertainty about subsurface conditions.
We will show Total Fluid Logs, which are based on comparing DNA signatures from produced fluids with a DNA stratigraphy log. Total Fluid Logs are utilized to 1) constrain interpreted fracture heights, and 2) work in combination with pressure and production data for Rate Transient Analysis (RTA) for significantly improved estimation of the half-length. The case histories will illustrate the differences between production rates and confirmed fracture height and half-length, and a discussion of microseismic is included.
We show how produced fluid collection during pad completions can elucidate well-well communication and demonstrate the impact of completion size and completion order on effective drainage heights. DNA changes in produced fluids can be compared to production data to reveal the timing and impact of frac hits between wells during zipper completions.
Finally, we provide a suggested workflow for analyzing water contributions out of target in the diagnosis of problem wells. Petrophysical logs can be compared to drainage height assessments to help reveal from which depths water may be producing and can be integrated with production data for a more complete subsurface understanding.
DNA diagnostics represent a complementary, cost effective, minimum environmental footprint and low risk tool for operators to easily integrate into existing production and engineering workflows for monitoring well health and subsurface conditions across time.
A Well Flux Surveillance and Ramp-Up Method for Openhole Stand-Alone Screen Completion
Authors: Karaaslan Mehmet , Wong George K. , Soter Kevin L. , Hicking Stephen H. , Yousif Majeed H. 
Published: October 2020

Abstract
Production and surveillance engineers need practical models to help balance out the reward of maximizing production and the risk of ramping-up the well too much that damages the completion. This paper presents a well flux surveillance method to monitor and ramp-up production for openhole stand-alone screen (OH-SAS) completion that optimizes production by considering risks of production impairment and screen erosion failure.
The flux surveillance model for OH-SAS assumes the filter cake from drilling operations does not cleanup uniformly during fluids unloading and production. This leaves pinholes on the filter cake that cause concentrated flows. The flux model contains three components: (a) capture and describe input properties of pinhole, (b) link completion pressure drop of flows across filter cake region and through pinholes to surveillance results from pressure transient analyses, and (c) distribute fluxes in the SAS wellbore. The model uses three input parameters to describe the pinhole diameter and internal filter cake properties. They are determined as a system utilizing the laboratory return permeability test, computerized tomography scans of test samples, and computational fluid dynamics simulations. The completion pressure drop incorporates radial and hemispherical flows and captures the compounding skin effects of pinholes with internal filter cake. Flux distributions are modeled as a network system. This contains branches and nodes that incorporate radial and vertical flow resistances in the annular region of SAS.
Application of filter cake properties and pressure transient analysis data showed the completion pressure drop as a function of flow rate is non-linear and higher with pinholes than without pinholes. By not incorporating pinholes in surveillance, operations can potentially limit ramp up when the completion pressure drop in the field is higher than predicted. Results from the network model showed the largest radial screen impingement velocity is at the top section of screen. The axial annular flow velocity or scouring velocity is two orders of magnitude larger than the screen impingement velocity. This warrants further considerations of wellbore enlargement and screen scouring erosion mechanisms due to the high axial annular flow.
Completion Sand Control Evolution in the Peng Lai Field, Bohai Bay, Offshore China
Authors: Zhou Leon , Xiu Haimei , Ma Yingwen , Chen Zenghai , Gong Yonggang , Xi Jiangjun , Tan Caiyun , Burton Robert , Gou Susan , Yu Xiang , Lyu Shi , Cao Jun , Du Fuyun , Tang Wenbo 
Published: October 2020

Abstract
This paper presents the evolution of completion sand control designs for the Peng Lai field in Bohai Bay, offshore China. A range of sand control designs from open hole gravel packs to open hole standalone screens to cased hole high rate water packs and cased hole frac-packs have been tried over time. Evaluation of these various designs indicates that single-trip multi-zone cased hole frac-pack designs provide the most effective method for developing the thick, low net-to-gross, unconsolidated sandstone reservoirs in this waterflood field. The paper describes the completion design and evaluation process used in the field and shows how field challenges led to innovative frac-pack downhole equipment designs.
This paper highlights completion practices and results from a range of well types used to develop the large Peng Lai field, offshore China. Development challenges and productivity and reliability results for alternative completion designs are discussed on the basis of field performance. The resulting preferred design, consisting of cased hole frac-packs using large-bore and small-bore single-trip multi-zone equipment are then discussed in further detail with emphasis on development of an innovative, fit-for-purpose, small-bore design. The installation challenges, solutions and successes experienced during the design and installation process and the actual performance and benefits of the new system are also discussed in this paper.
Over the course of Peng Lai field development, a range of alternative completion sand control designs have been tried and evaluated. Cased hole frac-packs provide the most effective completion method for the thick, low net-to-gross, sandstone reservoirs. To enable effective completions in sidetracked wells, a 7" single-trip multi-zone frac pack tool has been developed. This small-bore system has been used to complete 13 wells with 67 zones in the field to date, saving over 43 rig days and $8MM.
This paper presents reliability and productivity comparisons for a range of alternative completion sand control designs in a single, large field environment. A new 7" single-trip, fit-for-purpose design has been developed for the field and successfully implemented in multiple well campaigns. Installation time data and cost savings are provided.
Conformance Control in Fractured Tight Formations using Functional Nanocellulosic Materials Reinforced CO2 Foam Systems
Authors: Wei Bing , Chen Shengen , Tian Qingtao , Lu Jun 
Published: October 2020

Abstract
Foam stability primarily determines the efficiency of foam induced conformance control especially when fractures exist in formations. In this work, a well-defined nanocellulose fibrils (NCF) strengthened CO2 foam (NCF-st-CO2 foam) was proposed aiming to improve the conformance of tight formations with fractures. The bulk characteristics of NCF-st-CO2 foam including foamability, foam stability and texture were thoroughly investigated in a high-pressure and high-pressure (HT-HP) cell. Afterward, foam flow in fracture models was simulated intending to correlate the generated differential pressure with the parameters of foam quality, fracture aperture and fluid velocities, which helped to understand the foam generation, propagation and mobility in fractures. The capacity of this foam in controlling conformance and improving oil recovery in a fractured core was finally evaluated. Conventional CO2 foam (without NCF) was used as the reference throughout this work. The results indicated that the addition of NCF into CO2 foam considerably retarded the liquid drainage and bubble coalescence, which consequently stabilized the CO2 foam especially in the presence of crude oil. NCF-st-CO2 foam presented dense bubbles and a thick framework as observed visually from the cell. The flow behaviors of CO2 foam in fractures were largely determined by the bulk foam stability. NCF-st-CO2 foam could be generated in-situ (aperture from 0.5 mm to 5.0 mm) and then properly propagate throughout the fractures at a low mobility without any plugging issues. In core flooding tests conducted on the model with fracture aperture of 0.5 mm, the NCF-st-CO2 foam injection exhibited a delayed breakthrough, which finally led to an incremental oil recovery of 8.6% OOIP after conventional CO2 foam treatment. These results demonstrated the promising potential of NCF-st-CO2 foam in conformance control in tight formations.
Automatic Carbonate Rock Facies Identification with Deep Learning
Authors: Pattnaik Sonali , Chen Songhua , Helba Adly , Ma Shouxiang 
Published: October 2020

Abstract
Linking depositional properties and post-depositional diagenetic modifications of a rock with its petrophysical attributes remains a greatest challenge for carbonate rock characterization, formation evaluation and petrophysical rock typing. Generally, characterization of carbonate rock facies is labor intensive which requires an experienced geologist to interpret and integrate core, petrographic thin-sections and borehole image logs. In this approach, the carbonate lithofacies are identified with an emphasis on the diagenetic features, such as grain packing, micritization, cementation and dolomitization as well as diagenetic/karstic dissolution, and related connected or partial connected interparticle pores, intraparticle pores, separate and oversized vugs and micrite micro-porosity, etc. Here, we focused on developing deep learning based technique for automatizing manual facies identification process, a powerful tool to provide consistent and faster turnaround interpretations of geological facies for applications such as petrophysical parameter prediction.
In this paper, an architecture for unsupervised multi-class semantic segmentation of carbonate facies that incorporates deep U-Net based architecture is presented. The advantages of using such a network comes from adding skip connections which allows better flow of information in the network. This in return ensures comparable performances along with better feature representation for semantic segmentation tasks. Although many machine learning techniques have been previously applied for facies image analysis automation, the foundation is always the effectiveness of segmentation of multiple overlapping objects in the image. In case of carbonate rocks, diagenesis multiplies the heterogeneity complication. Therefore, in order to deal with this heterogeneity of carbonates we focused on unsupervised approaches because supervised learning methods can become very impractical due to the daunting task of manual feature labeling.
Multiple experiments are conducted on representative images of three types of carbonate facies (grainstone, rudstone, and packstone) to evaluate the performance of our segmentation algorithm and provide quantitative metrics useful for geological and petrophysical applications. Additionally, the segmentation algorithm is also used to detect primary resistive features from resistivity based borehole images. The consistent segmentation results have proved both the effectiveness and validity of the algorithm.