bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acoustic Data Driven Application of Principal Component Multivariate Regression Analysis in the Development of Unconfined Compressive Strength Prediction Models for Shale Gas Reservoirs
Authors: Iferobia Cajetan Chimezie , Ahmad Maqsood , Salim Ahmed Mohammed , Sambo Chico , Michaels Ifechukwu Harrison 
Published: October 2020

Abstract
Unconfined compressive strength (UCS) equally represented as geomechanical strength remains a critical mechanical property in the successful implementation of key technologies for shale gas reservoirs’ development and production. Attention has been less concentrated on prediction models’ development for shale geomechanical strength evaluation. Majority of the existing shale geomechanical strength correlations are dependent on single log input parameter, which is insufficient to account for the complex and non-linear behaviour of UCS across the entire reservoir interval of interest. The high relevance of UCS has therefore triggered the need for the application of an integrated system of principal component – multivariate regression analysis in driving UCS predictive models’ development for shale gas reservoirs. Generated acoustic datasets of notable shale gas reservoirs (Marcellus, Montney, Longmaxi and Roseneath) in respective countries (United States of America (USA), Canada, China and Australia) were used. Statistical test analysis was conducted in validation for wider applications of the developed UCS prediction models.
Models development were driven by 21,708 datapoints of acoustic parameters, models’ accuracy ratings were above 99%, R-squared values had high degrees of closeness to unity, mean absolute percentage error (MAPE) values were at less than 10% and coefficient of variation (COV) at less than (1.0). UCS prediction models were all dependent on multiple direct log measured acoustic parameters in distinction to existing UCS empirical correlations; thus, a pure reflection of significant boost to the accuracy and reliability of UCS measurements for shale gas reservoirs. The developed prediction models will promote geomechanical strength accountability and lead to creation of a robust base in minimization of wellbore instability problems, optimization of wellbore trajectory and containment of hydraulic fractures. This will significantly contribute in putting gas resources of shale reservoirs with enormous potentials, at the forefront of quantitatively meeting natural gas requirements in global energy demand.
Quantifying Fracture Interference and Allocating Load Recovery and Hydrocarbon Production in Various Well Configuration Using Chemical Tracers
Authors: Tian Wei , Darnley Alex , Dempsey Chris 
Published: October 2020

Abstract
This paper presents a method of using chemical tracer data to quantitatively evaluate interwell communication in various well configurations by combining raw tracer data with field production data. Qualitatively, tracer breakthrough in an offset well indicates that there is communication between the infill and primary well, but by utilizing the method described in this paper, the fracture driven interaction (FDI) severity can be quantified by determining the total mass of tracer produced out of the primary well and new wells. The technique yields an allocation of load fluid and hydrocarbon recovery at each well and associated formation.
Sensor Fusion and Machine Learning Techniques to Improve Water Cut Measurements Accuracy In Multiphase Application
Authors: Barbariol Tommaso , Feltresi Enrico , Susto Gian Antonio , Tescaro Diego , Galvanin Silvia 
Published: October 2020

Abstract

Objectives/Scope
A Multi-Phase Flow Meter (MPFM) performs Water Liquid Ratio (WLR) estimations using a dedicated sensor relying on one physical principle (e.g. electrical impedance). The accuracy of the WLR sensor might also be dependent on the flow properties. An approach based on Machine Learning techniques and multi-sensors data fusion has been implemented to enhance the reliability and accuracy of the WLR estimations in Multi Phase application using onboard sensor measurements of a MPFM.

Methods, Procedures, Process
In order to improve the estimations of multi-phase applications, we exploit the availability of historical data collected with heterogeneous sensors; the underlying idea of the proposed approach is to exploit such data with Machine Learning supervised techniques to provide accurate measures. In this work we compare modern supervised learning approaches like Random Forest, Gradient Boosting techniques and Kernel methods. The proposed methods have a relatively simple form that can be deployed also in embedded applications.

Results, Observations, Conclusions
In this work, we will show through extensive experiments that the proposed approach could improve the original estimations. The algorithms underlying the proposed approach have been trained using data collected at flow loops test facilities with different flow conditions. The best model has been chosen not only for its predictive performances, but also looking at the computational time needed to make a prediction and considering its robustness to outliers. As expected, depending on the dataset numerosity, the best performing model can change: we provide experimental results for various dataset sizes in order to help practitioners choose the best regression method depending on the available data numerosity. An additional considered aspect is the computational time of the various approaches, which may be a relevant characteristic to be evaluated before rolling out productive solutions.

Novel/Additive Information
To increase the accuracy of MPFM, a sensor fusion technique that benefits from the many measurements collected by the MPFM, has been developed. Many different models have been compared on: prediction performances, confidence interval, robustness to outliers, execution time. The resulting model provides enhanced estimations equipped with confidence intervals that can be used for prediction quality assessment and associated risk management.
Can Paraffin Wax Deposit Above Wax Appearance Temperature? A Detailed Experimental Study
Authors: Aguiar Janaina I. , Nerris Anthony A. , Mahmoudkhani Amir 
Published: October 2020

Abstract
Shale producers in North America continue to face challenges with paraffin wax deposition in well bores, production tubing and flowlines as production declines and artificial lift methods such as gas lift are applied. Preventative measures are often based on traditional screening and assessment methods which rely on crude’s wax appearance temperature (WAT). Recent studies have shown that operators are increasingly turning to costly remediation program and stimulation jobs to maintain production rates as oil wells decline at faster rates than anticipated. It appears in many cases that these declines can be exacerbated by downhole organic fouling and wax deposition that surprisingly may have occurred above the original WAT. This paper aims to conduct a root cause analysis to identify the key internal and external factors impacting an oil’s WAT. Four crude oils and field wax deposits from Gulf of Mexico, South Texas and West Texas areas were used for this study. Due to the unique composition of the field deposits and depleted crude sample available for analysis, a novel strategy for lab screening was adopted. Crude oil samples were examined "as received" and after reconditioning via the addition of solvents and/or field deposits. Further measurements were performed to identify and quantify the effects of the addition of production chemicals particularly hydrate inhibitors. A series of dispersion and deposition tests were then conducted in order to demonstrate the potential impacts of incompatible chemistries. Fractionation based on solubility revealed a dynamic behavior for the WAT of crudes that corelated to their paraffin content and distribution. Samples containing C45+ paraffin chains demonstrated significant WAT changes upon reconditioning. The addition of methanol and MEG typically used as hydrate inhibitors, impacted the WATs of the crudes as measured by DSC showing increases in the WATs compared to unconditioned samples. An additional suite of dispersion and deposition testing conducted on the same samples supported the findings of the DSC studies. A nonlinear correlation was found between increasing WAT and paraffin deposition rate. The approach outlined in this paper provides a more accurate field condition simulation leading to more effective inhibitor selection and ultimately improved flow assurance management in more complex production systems. The methodology of mimicking hydrocarbon fluid behavior was the key factor identified by the root cause analysis and correlated to the series of past failures attributed to organic fouling. WAT was found to be a parameter that can vary for a crude oil over the life of a production system. With the current evolution of shale oil production in North America and around the world, we expect these findings will also be relevant to unconventional production and will aid the oil industry to anticipate and manage previously unforeseen problematic paraffins in production systems.
Automated Gas Lift System Optimization Through Combined Data Analytics and Wellbore Modeling Approach: A Case Study in an Offshore Middle East Oilfield
Authors: Surendra Meher , Deng Lichi , Kansao Rami , Davani Ehsan , Darabi Hamed , Castineira David 
Published: October 2020

Abstract
Systematic and comprehensive analysis of gas lift operations must include historical performance evaluation and heuristic diagnostics for identification of suboptimal performers. Reliable nodal analysis models are necessary to optimize the gas lift systems. Though some degree of automation is currently possible, this process still takes substantial effort and time. The work presented here is a streamlined, repeatable, and automated workflow to diagnose and optimize gas lift operation through analytics, reliable wellbore modeling and calibration that reduces the time from days to hours. The workflow is also very easily repeatable, which makes it very efficient to update when new data is available.
The workflow combines the emerging data analytics efforts in the oil and gas industry along with traditional gas lift optimization guidelines to identify artificial lift-related key recovery obstacles and corresponding development plans. This novel combined approach proves the effectiveness and necessity of augmented artificial intelligence, and the case study of field analysis and execution further endorse the impact of such optimization workflow for gas lift assessment and operations. It builds from data-driven and engineering-based workflows that compute smart metrics and dashboards based on the historical well production and the gas lift systems performance. Several KPIs and metrics are utilized, some of which are industry standard while others are intuitively built exclusively for this workflow.
In this paper, we present the results and challenges in applying this workflow to an offshore Middle East oilfield, with over 50 gas lift wells. As always with field data, several aspects of the data had to be addressed. Accuracy and relevancy of data was questionable. We developed several workflows to address the missing/incorrect data to ensure the robustness and credibility of the results from the workflow. Due to the practical implication of the analysis, stringent quality control and error margins were maintained throughout the analysis. No nodal analysis models were available, consequently models were built and calibrated as a part of this effort. Overall field and facility constraints were also incorporated to ensure results were achievable in the field.
The optimized operational plan would yield an additional 15 to 20% gains over current production rate. Further benefits by upgrading the gas lift design are also quantified using the proposed workflow. The time required to construct, calibrate, and optimize around 50 wells was less than two weeks.
Novel Class of Zinc-Free Heavy Clear Brines for HPLT Applications
Authors: Liimatta Eric , Lambeth Gregory , Liu Yunqi , Messler David 
Published: October 2020

Abstract
In an effort to increase oil production over the past twenty years, the oil industry has moved into deep water for new field developments. During this time, progressively deeper wells containing geopressured reservoirs require the use of high density completion brines. Many of these reservoirs require the use of completion brines above 14.7 ppg. Historically, zinc bromide solutions have been used to provide the required density and true crystallization temperature (TCT) for these reservoirs. However, health, safety, and environmental concerns around zinc bromide have caused operators to seek alternative solutions.
The oil and gas industry uses a large amount of heavy brines in the completion of high pressure-low temperature (HPLT) wells, and there are currently very limited options for high density (&gt;14.7 ppg), low TCT fluids that do not contain zinc. One option, cesium formate, is prohibitively expensive, and the other, based on existing halide technology, has a lower density limitation than this new class of clear brine fluid.
This paper describes a novel, zinc-free, high-density, low true crystallization temperature brine fluid developed by Albemarle Corporation. We will discuss the testing performed to characterize this new fluid. This new class of brine fluid fills a gap in fluids technology for a fluid suitable for high pressure, low temperature applications at higher densities than previously available. Further, this is accomplished in an environmentally friendly heavy brine fluid that contains no Environmental Protection Agency (EPA) listed materials.
Making Digital Collaboration More Efficient and Reliable With Second Generation Standard Interoperability Protocol
Authors: Neri Philip , Philo Ross 
Published: October 2020

Abstract
The rapid pace of digitalization of E&P operations is generating a sharp increase in the volume of data being exchanged between different disciplines, departments, partners and vendors. To facilitate the exchange of data across these many entities rapidly and in a uniform manner and with no loss of information, the ETP (Energistics Transfer Protocol) WebSocket-based two-way protocol, originally introduced in 2014 and updated in 2016 (McKenzie 2016) to support streaming of real-time data from drilling rigs to onshore monitors, has been expanded.
The standard ETP data streaming protocol delivers a near-instant streaming of data from rig to onshore centers with the additional ability to entertain multiple connections to perform the streaming to several recipients concurrently. Modes were also present in that version to make use of the protocol for direct application interoperability. However, the latter were not fully developed and as a result saw limited uptake after the publication of the standard. The new version of the protocol aimed at improving the real-time data streaming capabilities based on the experience accrued over 3 years of deployment, as well as completing the interoperability features and adding new sub-protocols to support the interrogation and selective retrieval of data from a server as well as other actions that facilitate direct application interoperability. A data system or application can be set to signal to listening systems instances of a new object or changes to existing objects. Each interoperability connection is initiated by an application acting as a client and connecting to an application that is an active server at that time. Once the connection is in place, the duplex or bi-directional nature of the protocol stack makes it possible for either application to make requests to the other one.
These application interoperability capabilities offer an alternative to the existing data transfer process that uses files as a mechanism to move data from one application to another. The file-based process remains relevant for transmittals that require the creation of a data package that can be loaded into systems not connected to the same network, as well as for archival purposes.
The Effects of Geomechanics, Diffusion and Phase Behavior for Huff-n-Puff IOR in Shale Oil Reservoirs
Authors: Zheng Shuang , Sharma Mukul 
Published: October 2020

Abstract
Lab experiments and numerical modeling have indicated that gas injection for IOR in tight oil reservoirs is technically feasible. Several operators have conducted pilots in the Eagleford and Bakken shales for a huff-n-puff IOR strategy with mixed results. Our objective in this work is to study the impact of (a) geomechanical effects during injection and fracture closure during production, (b) injection rate effect during huff-n-puff processes, (c) timing effect in huff-n-puff process, (d) phase behavior effects for huff-n-puff oil recovery, and (e) impact of soak time on oil recovery.
We developed and utilized a fully coupled geomechanical compositional fracturing/reservoir simulator for gas injection in tight oil reservoirs. The model calculates stress changes due to both poroelastic (pressure changes) and mechanical (fracture opening and closing) effects. Permeability hysteresis during loading/unloading cycles is also considered. The simulation procedure involves the following steps: (a) The well is produced for a period of time (b) Gas is injected into the well and this is simulated by specifying the injection rate and gas composition (c) The well is produced again after some soaking period, and the results are checked for any improvement in the oil recovery.
Based on our simulation studies, we observed several important trends. The degradation of permeability over multiple loading/unloading cycles due to hysteresis together with the poro-elastic effect severely impacts the oil recovery in later cycles. It was found that incremental oil recovery decreases after several huff-n-puff cycles. It was found that if higher gas injection rate gives higher oil recovery, but the oil recovery does not increase linearly with the injection rate. Phase behavior is found to be a key factor in the oil recovery and rich gas injection gives the best results. The impact of timing to initiate the huff-n-puff IOR was also studied. The soak time increases the ultimate oil recovery, but its impact is not significant in the study.
Our simulation results provide operators with significant new insights on the design of huff-n-puff IOR. It is shown that fracture widening and closure during huff-n-puff cycles have a significant impact on oil recovery. The novelty of the work is the development and use of compositional geomechanical model for IOR performance evaluation.
Production Rate Measurement Optimization using Test Separator and In-Well Sound Speed
Authors: Ünalmis ö. Haldun 
Published: October 2020

Abstract
The litmus test for downhole multiphase flowmeters is to compare the measured phase flow rates with the test-separator rates. In most cases, the composition of the measurand is required for flowmeters and is typically obtained from bottomhole fluid samples. Extracting and analyzing fluid samples is an expensive process mostly done at the initial stages of field development. Because the composition is not always available and is usually old, flowmeters may have subpar performances when compared to test separators. In this work, it is shown that once the test-separator data are available, measuring mixture sound speed downhole will ensure the optimization of a multiphase flowmeter system without needing to obtain new fluid samples. This novel method is demonstrated in a North Sea case history.
This method of optimizing flow rates is independent of the measurement device because the required flow velocity and sound speed measurements could be obtained from separate and generic devices. For example, the fluid bulk velocity and mixture sound speed could be measured by a point measurement device and a distributed acoustic sensing (DAS) system, respectively. The main challenge in a flow-velocity/sound-speed measurement system is to determine individual phase sound speeds so that the mixture phase fraction could be correctly modeled using Wood's mixture sound speed model. The phase fraction from the separator tests could be used as the target value to optimize the performance of the system. This is done with a backward calculation of the sound speed of individual phases. Pressure and temperature variations at measurement locations as well as the pipe compliance effects are also accounted for in this approach. Following the adjustment of individual phase sound speeds, a forward calculation using the same model yields a phase fraction close to the actual value, which could be improved further by an iterative approach.
A downhole optical flowmeter in a North Sea field measured mixture velocity and sound speed. Well test results indicated that the water cut was underreported and phase flow rates did not match the test-separator rates. Instead of halting production and going through a fluid sample analysis cycle, the test-separator water cut was used as the target value to optimize oil phase sound speed using Wood's model with a backward calculation. The difference in oil sound speeds was extrapolated to other pressure and temperature conditions, and forward calculation showed that separator tests and flowmeter measurements closely matched. Subsequent flowmeter and test-separator data confirmed excellent agreement.
Using well test data and sound speed to optimize phase flow rates is a novel method that has not been previously demonstrated. This method is independent of device type, broadly applicable, and furthers the understanding of multiphase flow measurement.