bluuebunny/crossref_metadata_2025_split huggingface dataset:
Workflow for Optimization of Waterflood Performance: Field Example from El Furrial Field - East Venezuela Basin
Authors: Perez Amaro Iris , Chek-Yan Chelhod Nellys , Laprea-Bigott Marcelo 
Published: July 2020

Abstract
This paper presents a workflow for the analysis and diagnosis of the water injection project and the evaluation of chemicals treatments for water control using laboratory data and numerical modeling for field performance analysis, oil recovery and sweep efficiency optimization in the East Area of SJN FUL 2 reservoir / El Furrial field - Venezuela.
A description of applied chemical technologies and considerations for selecting well candidates to workover based on root-cause analysis of water production, along with reservoir modeling of feasible production scenarios and its effect on the hydrocarbon recovery, are shown on the second part of this study.
The optimization process on the production and injection profiles and the impact on the recovery of the remaining oil reserves using the proposed workflow are demonstrated with El Furrial field and SJN FUL 2 reservoir information. The results show that reservoir management is the main driver behind the well performance control in this high heterogeneity water flood system. The use of proper production surveillance management techniques, allows selecting technologies which will bring a project to success when heterogeneities play a fundamental role.
Corrosion-Fatigue Assessment of Martensitic Stainless Steel and High Strength Low Alloy Steel Sucker Rod Materials in Harsh Environments
Authors: Barreto Rodrigo , Martinez Oscar , Shakhmatov Aleksey , Stachowiak John , Howie William , Badrak Robert 
Published: July 2020

Abstract
The cyclic nature of the operations with sucker rods makes the fatigue life one of the most desired characteristics for these products. Production in aggressive service environments with higher acid gas concentrations associated with increased levels of hydrogen sulfide (H2S) and carbon dioxide (CO2) requires good fatigue life with the potential for corrosion. This combination makes corrosion-fatigue the primary mechanism responsible for sucker rod failures in reciprocating artificial lift applications.
High Strength Low Alloy (HSLA) steels have been widely used in decades to provide fatigue resistance, however the corrosion resistance of such steels is of concern. High-chromium steels have recently been utilized to improve performance, but their corrosion resistance is limited along with their fatigue performance.
The development of a new martensitic stainless-steel grade aims to improve corrosion resistance, extend fatigue life of sucker rods and reduce overall operating costs. The combination of selected chemistry with well-defined process variables and heat treatment parameters is key to provide a product with optimum properties that includes fatigue life with needed mechanical properties, toughness and grain structure.
This paper presents the results of different high strength low alloy steels compared to a targeted stainless steel in corrosive environments.
A Sustainable Future Under Energy Intensity Scenarios-Peru's Compliance with COP24 in an Energy Trilemma Environment
Authors: Cervantes Bravo Reynaldo José , Jimenez Nieves Edgar , Valqui Ordoñez Brayam , Canto Espinoza Daniel , Hinostroza Cairo Anderson 
Published: July 2020

Abstract
The most important transition of the energy matrix in Peru was characterized by an economic bonanza between the years 2009 - 2011 and, whose energy intensity (I.E) was reflected by the accelerated growth of the GDP; which, caused an exponential increase in the energy demand of Peru and, whose multiplying effect was produced by the energy exchange of the predominance of Hydroelectric Energy towards the development of Natural Gas with the Camisea Gas Megaproject, however, it was not considered the impact of other factors.
In this sense, the present study requires contextualizing the Energy Trilemma: 1) the country's energy security; through an energy efficiency policy in response to meeting demand in line with GDP growth, 2) energy equity, for the access of quality energy and accessible prices to more vulnerable populations with a diversified energy matrix; 3) environmental sustainability, to describe the Environmental Commitments of Peru with the COP24.
The methodology is based on a macroeconomic-energetic model, whose architecture begins with historical information between the years 1970-2016 with respect to GDP vs. primary energy consumption; to then calculate the annual energy intensity of Peru and its CO2 emission according to the polluting factor of each primary matter. Followed, using projections of the GDP from 2017 to the year 2035 (3.8% per year - Conservative case with information from the World Bank) and 3 scenarios of decrease in energy intensity of 1%, 1.5% and 2% per year, may increase energy efficiency and reduce the emission of CO2 in the proportion of 10.4%, 15.2% and 19.6% respectively between 2017-2035.
As a result, the total energy consumption will be estimated up to the year 2035 in Millions of TOE, according to each scenario of variation in energy intensity (ΔI. E). and with the forecasts in the distribution of the energy matrix of the years 2025 and 2035 through the BAU methodology, its forecasts of each primary material will be known (Natural Gas, Oil, Coal, Hydroelectric, Renewable Energy, others) until the year 2035 This will allow us to know the forecast of CO2 emissions based on each pollution factor of the primary sources and energy intensity levels predicted with respect to the base case (Δ IE = 0).
Finally, Peru's commitment to COP24 will be evaluated based on the per capita correlation of the country / world population in the estimation of the cumulative maximum emission limit of 2.13 GtCO2 between 2017-2035 for Peru, if the temperature is not increased more than 2 ° C for the year 2100 and guarantee the demand of Peru under an optimized Energy Trilemma.
Using Machine Learning for Building Multivariate IPR Models From High Frequency Streaming Data
Authors: del Pino Fiorillo Mario Antonio 
Published: July 2020

Abstract
Over the last years, we have seen how the volume of data being generated at the field has grown exponentially. Dealing with this amount of information has become increasingly challenging, as conventional computational tools are not suited for dealing with such high-frequency data. This is more noticeable on modern assets where SCADA systems are the norm and several gigabytes, or even terabytes, of data are generated each year. On this work, we explore an innovative way to address this issues when trying to build productivity models for an asset. We compare this with conventional workflows and explore the benefits from it.
The objective of this work was to develop an algorithm capable of building IPR-like models with high-frequency data generated from producing wells. The code handles data retrieval, filtering, model fitting and results presentation. It accounts for depletion driven changes and is able to accurately predict short and medium term evolutions in productivity. This would allow integrating the developed code with a global optimization model intended to be developed. The Multivariable approach allows including effects of other variables if necessary, such as ambient conditions, depletion rates, sensors re-calibration, among others.
This technique was tested on a gas condensate field constrained to a daily nominal rate of around 200MMscfd of dry gas. The market constraint forces the operations team to carefully manage producing wells to maximize cash flow. Production strategy focusses mostly on condensate maximization, prioritizing wells with the highest liquid yield. This usually results in having three wells on stream of all six available. Because of this, those wells with low liquid yield have limited production data. It is on these wells were conventional IPR regression workflows suffer from representativeness issues. This would result in being unable to properly run a global optimization model taking into account all available wells.
Machine learning techniques were used to create multiple models. Metrics were computed to quantify model accuracy and select the best fit model. The resulting code successfully built IPR curves with great prediction capabilities. This was validated by creating different models for each month from March 2018 to July 2019 using data corresponding to their previous months.
This approach gives a tool to automate the creation of mathematical models for data-rich assets. It shows how data analytics techniques allow building versatile programs that can be rapidly deployed in the field. There are numerous potential applications; real-time production strategy updates, integrated interpretation of asset development, better design and scheduling of surface facilities, among many others.
Improving Oil Recovery in the Mature La Cira Infantas Oilfield Using Multiphase Flow Simulation
Authors: Yao Calvin C. , Blanco William F. , Alvarez Argenis J. 
Published: July 2020

Abstract
La Cira Infantas (LCI) is the oldest oilfield in Colombia, with 100 years of oil production history spanning three periods: primary depletion, the first waterflood (WF) period, and the second WF period. After the first WF period, the field had neared its economic limit and an abandonment program was underway from 2003 to 2005.
Partners Ecopetrol and Occidental made the decision to implement a waterflood redevelopment project in 2005 that included reconfiguration of the old WF areas and WF expansion into new areas. The partners formed integrated reservoir development and management teams (RDT and RMT) that have combined workovers, infill drilling, WF optimization, and other IOR/EOR methods. The oil production has increased from approximately 5,000 to over 43,000 bopd, which is above the predicted 40,000 bopd.
One of the key components was WF conformance controls to improve sweep efficiency and to improve injection profiles across multiple stacked-sand layers. The selective injection method was implemented using multiple packers and side-pocket mandrels/valves. The design of selective injection strings involved complex engineering calculations and left some room for improvement.
This paper describes a new workflow used to optimize the selective injection design using multiphase flow simulation based on actual WF patterns. A field pilot test used an inverted 5-spot WF pattern to demonstrate this new workflow. The simulation results predicted a 45% incremental oil production above the base case. The simulation optimization process reduced the number of mandrels/valves from nine to six, which saves about 30% of the associated completion costs compared with the initial completion proposal for this case study.
Risk Mitigation in Overpressured Wells Through Geomechanical Approach
Authors: Carvajal Olga , Sierra Camilo 
Published: July 2020

Abstract
The geological evolution of the northern region of Colombia has resulted in lateral/oblique compressional stresses leading to the development of tilted faulted blocks and associated subsidence, followed by intense folding/uplift that has impacted the stress regime and the pore pressure evolution. Previous pore pressure prediction approaches often considered a shallow overpressure associated with a high fracture gradient (FG), and thereby allowing an assumption for a wide operational mud window. Drilling experiences however, indicated a contrasting scenario and thereby a better understanding and a more accurate estimation of overpressure and FG became necessary.
With the aim of optimizing drilling operations, a geomechanics analysis was conducted, based on existing information incorporating local geological inputs and production well data, in order to better predict wellbore stability, pore pressure, fracture gradient and to identify related drilling risks for the planned wells.
The resultant geomechanical model confirmed the presence of shallow overpressure development, the occurrence of which, along with its distribution and magnitude, is mainly controlled by the geological events (especially uplifting) that shaped the basin. Subsequent updates of the model managed to explain the frequent occurrences of water inflows, gas kicks, high volume of losses (a gain-loss situation) and instability recorded in previous wells. It also predicted a feasible but conservative mud weight window for the wells in this field. Understanding the geological evolution allowed for a better comprehension of the stress regime changes over time. Through an enhanced understanding of the geological and geomechanical influences, a risk mitigation strategy was developed to proactively reduce pore pressure and wellbore instability related drilling effects encountered in the earlier wells.
So far, well schematics and mud weight windows have been optimized in five wells through utilizing this geomechanics approach to deal with the challenges imposed by a narrow mud window. The planned wells have been successfully drilled in record time with no major drilling problems by using the recommended mud pressures and optimized drilling practices.
RTA Assisted History Matching with a Combined Hydraulic Fracturing and Reservoir Simulator
Authors: Fowler Garrett , McClure Mark , Allen Jeff 
Published: July 2020

Abstract
History matching is a critical part of the simulation workflow. History matching in shale is unique because it involves data from both hydraulic fracturing and from production. This paper presents a step by step workflow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. We perform sensitivity analysis simulations using a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop ‘motifs’ that inform the history matching process. Using intuition from these simulations, we show how to expedite history matching by changing matrix permeability, fracture conductivity, matrix pressure dependent permeability, boundary effects, and relative permeability. Finally, a worked example, using two Eagle Ford hydraulically fractured wells, is presented to demonstrate the workflow.
Fluid Sampling, Analysis and Its Relation with Multiphase Flow Meter Management for a Gas Condensate Field in the Peruvian Jungle
Authors: Tavara La Chira Ricardo Yashin , Segovia Nolasco Gissela Carolina 
Published: July 2020

Abstract
In the K Asset, the production allocation methodology is based on mass measured by a multiphase meter and chromatography analysis of well production stream. In this process, many steps are involved in order to get the required information for allocation purposes. From operational procedures for collecting samples and calibrate the meter in field, to verify laboratory result and adjust equations of state.
The main objective of this paper is to present a structured methodology to update chromatography composition based on field sampling service experience and how these conditions and results interact with the information required to update fluid models uploaded on a radioactive source multiphase meter (MPFM).
As a first step, this paper will cover the process to obtain representative single-phase samples for gas and condensate phases, based on recommendations to be followed during its collection. Afterwards, the reviewing process for fluid validation report in order to establish steps for selecting the set to be recombined. Once the recombination is executed, additional controls need to be performed for determining success on the process.
The integration of some of this topic with MPFM requirements will lead to use of fluid surveillance results as input for some uploaded information in the meter in order to guarantee the best and more accurate possible measurements.
Water Flowback RTA Analysis to Estimate Fracture Geometry and Rank the Shale Quality
Authors: Ibrahim Ahmed Farid , Assem Ahmed , Ibrahim Mazher , Pieprzica Chester 
Published: July 2020

Abstract
Recently, flowback data analysis enabled us to evaluate important fracture parameters including fracture conductivity and volume in unconventional reservoirs. To perform the analysis, diagnostic plots, straight-line techniques, and history-matching techniques have been used. Immediate water and gas production usually occurs on flowback in shale gas wells. In this paper, a novel workflow is developed for the analysis of water flowback data and early-time production of shale gas wells. This analysis then helps to define the movable water and the applicability of the soaking process on the shale gas well.
Rate transient analysis (RTA) combined with decline curve analysis (DCA) was used to analyze different shale gas wells. Effective fracture volume and geometry were calculated from the RTA analysis. Estimated ultimate water recovery was calculated from DCA. The calculated water-in-place and the estimated ultimate water recover (EURw) will be compared against the injected fracturing fluid.
Water RTA result show that in the case of shale wells with no movable formation water, gas kick off early, and boundary dominated flow (BDF) was observed. In addition, these wells performance improved with soaking process. On the other hand, if initial formation water saturation is higher than the connate water, water production will be from the frac fluid and formation water. As a result, gas kick off delays and transient flow regimes are expected. Soaking process can have a negative impact on the well performance if the movable water saturation is high.
Honoring the flowback data can help to estimate the fracture geometry and to judge the quality of the shale formation quality and its validity for soaking process.
Unlocking Oilfield Development using Blockchain
Authors: Vera Fabian 
Published: July 2020

Abstract
Many operators and service providers integrate oilfield data in the form of data hubs. Third party vendors often control these data hubs in the oil and gas industry, and data quality is often challenged. The digital gentrification of legacy wells and optimization of subsurface data management for exploration is at reach by applying smart technology ledgers, better known as blockchain.
Blockchain technology provides a platform to keep all data linked. The data is smoothly shared among different groups like exploration, drilling, completion and production. Over time, some data might be lost of which the inheriting company might not be aware. If all the data is loaded onto the blockchain, it becomes easy to track.
When we deal with subsurface analysis in offset well analysis, the data of all wells in a field (or a nearby field) readily become available on a blockchain database to assist in better decision-making. The data generated are stored in blockchain and can act as historical data in the future to better train models and assist in making informed decisions in both green and brown fields, as in subsurface analysis. For instance, a reservoir characterization project or a determination of landing zone progresses smoother when all historical data are available for review.
Another opportunity for blockchain is in the field of legacy wells. When acquisition of wells happens, the inheriting company receives data, and some of which is still in traditional paper report format. Over time, inheriting companies might not be aware of the risk of losing 25 to 40% of these legacy data. If all this data is loaded onto the blockchain, it becomes easier to track the history of these legacy wells, and they can be better optimized.
On production inventories, the immediate effect translates in the real time quantification of reported production and the inventories, which have an instantaneous effect on the price of commodities like WTI crude or Brent crude instead of waiting longer to correct estimated values.
The carbon negative narrative also offers a unique opportunity to connect private investors with operator leadership. As capital is at a premium to sustain development campaigns in the Permian basin and as more incentives in the form of tax credits become available for CO2 injection, investors need a tool to sustain the trust where they deploy their capital. By generating smart contracts in blockchain as a medium to elevate trust and involve players immediately as they report volumes of CO2 injected, they have a first view at how many tax credit incentives they become eligible to claim.
Application of Blockchain represents an opportunity for digital gentrification across upstream, midstream and downstream in the energy sector. It also acts as a tool to connect and accelerate environmental efforts. The quicker its adoption, the larger the benefits and profits become.
Factor that Influence on Ultra-Orthodox Jewish Mothers’ Service Satisfaction with Obstetric Care in Israeli Public Hospitals
Authors: moryossef Iris gertner 
Published: July 2020

Abstract
The Ultra-Orthodox sector is a uniquely conservative and closed community within Israeli society, which makes up 8-11% of the population of Israel. The Ultra-Orthodox live in separate neighborhoods that constitute closed cultural quarters with little interaction with the non-orthodox society. Within these neighborhoods, they maintain their traditional lifestyle, which adheres to strict, Torah-based Jewish law. Observance of Jewish laws and customs is closely monitored and access to the internet and the general media is limited, keeping the exposure to undesired contents at the minimum.
Religious law observance includes modest dress codes, complete separation between male and female that begins in early childhood, and strong family values, including marrying young and aspiring to form a big, close-knit family unified around the traditions of the community. The men in the Ultra-Orthodox society dedicate themselves to full-time religious studies in specialized institutions (the Kollel ), while the women oversee the family income, children's education and all domestic matters. Whereas 61% of Ultra-Orthodox Jewish women are employed, only 52% of the men have a job, while the other men dedicate their time to studying the Tora. On average, an Ultra-Orthodox woman gives birth for the first time at the age of 19, and the overall fertility rate among this population is three times that of the secular population (8.5 children versus 2.9