bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Nanoparticles on the Interfacial Tension of CO2-Oil System at High Pressure and Temperature: An Experimental Approach
Authors: Al-Anssari Sarmad , Arain Zain-UL-Abedin , Shanshool Haider Abbas , Ali Muhammad , Keshavarz Alireza , Iglauer Stefan , Sarmadivaleh Mohammad 
Published: November 2020

Abstract
In the recent decade, injection of nanoparticles (NPs) into underground formation as liquid nanodispersions has been suggested as a smart alternative for conventional methods in tertiary oil recovery projects from mature oil reservoirs. Such reservoirs, however, are strong candidates for carbon geo-sequestration (CGS) projects, and the presence of nanoparticles (NPs) after nanofluid-flooding can add more complexity to carbon geo-storage projects. Despite studies investigating CO2 injection and nanofluid-flooding for EOR projects, no information was reported about the potential synergistic effects of CO2 and NPs on enhanced oil recovery (EOR) and CGS concerning the interfacial tension (γ) of CO2-oil system. This study thus extensively investigates the effect of silica NPs on the γ of CO2/decane system at elevated pressure and temperature to recognise the potential impact of NPs-injection on the future CGS projects.
To achieve this, a wide-ranging series of tests have been conducted to reveal the role of hydrophilic and hydrophobic silica NPs on γ of the CO2/oil system. n-decane was utilized as model oil and different amounts of NPs were mixed with the oil phase. Oil-NPs dispersions were formulated using an ultrasonic homogenizer. The γ of the CO2/oil system was measured at different pressures (0.1 to 20 MPa) and temperatures (25 to 70 °C) using a high-pressure temperature optical cell. The γ data were measured using the pendant drop technique via axisymmetric drop shape analysis (ADSA).
The results showed that, generally, CO2/oil γ subjected mainly to pressure, temperature, and with less extent to NPs load in the oil phase. γ decreases with increased pressure until reaching a plateau where no more significant decrease in γ was observed. The γ trend with increased temperature, on the other hand, was more completed. No significant impact of temperature on γ was recorded with low pressure (≤ 5 MPa). Similarly, at relatively high pressure (≥ 25 MPa), only a slight variation of IFT with temperature change was recorded. However, for the pressure range from 5 – 25 MPa, IFT was increased remarkably with temperature. Furthermore, NPs in the oil phase exhibit a remarkable influence on IFT. In this context, the presence of hydrophilic silica NPs in the oil phase can significantly reduce the γ of the CO2/decane system. However, hydrophobic silica NPs showed less influence on IFT reduction.
The outcomes of this work afford good understandings into applications of NP for EOR and CGS applications and help to de-risk CO2-geological storage projects.
Instrumented Docking Perforating Gun System: Field Experience Results in Ecuador
Authors: Vasconez Danilo , Pastrana Wilmar , Benavides Marcia , Vallejo Renato , Dahik Carlos 
Published: November 2020

Abstract
Schlumberger implemented the first instrumented platform integrating advanced measurements in real-time while perforating with plug-in gun connections. This paper presents the results obtained at field level using this technology in Ecuador and a comparison of this technology with perforating operations and techniques in terms of efficiency, reliability, and correlation between the predicted and measured dynamic events to perform a full evaluation of the perforating design process, execution and its impact in well performance
Out of more than 120 jobs performed year to date in Ecuador, the authors selected 5 wells covering operations with dynamic underbalance (simultaneous and standalone) and gas fracturing to illustrate the efficiency gains of a faster and more reliable arming system and to analyze the match between simulated and measured dynamic events (pressure and shock). Among the 5 wells, two of them achieved positive production results, and the real time measurements showed additional information in terms of: reservoir pressure, well response, and perforations cleanup during the job execution. The other three wells provided key information related to well control and pressure buildup after perforations, withal they did not provide comparatively consolidated production results.
Can a Cube Model Mitigate Parent-Child Effects and Improve Oil Recovery in the Permian Basin
Authors: Yan Qiyan , Zheng Wei , Biniwale Shripad , Banerjee Raj , Xu Tao , Rakhimova Yulia 
Published: November 2020

Abstract
As the shale development activity in the Permian continues to be strong and oil prices recover, increasing numbers of infill child wells are being drilled as operators want to improve recovery from each section and continue to meet their production targets. However, production data suggests that both parent and child wells suffer from production losses if they are located too close to one another.
The cube model concept, which is also referred to as supersize fracturing, was first introduced about two years ago and has been piloted in the Permian Basin. In a cube model, multiple wells, usually more than 30 horizontal wells with five to six wells in each different horizontal layer, are drilled and completed in the same section. Operators produce those wells simultaneously with the objective of mitigating the parent-child effect of unconventional reservoirs.
Nevertheless, with all wells producing at the same time and competing for production from the first day, will this benefit ultimate recovery? This question was investigated through comprehensive fracture and reservoir modeling and simulation. A reservoir dataset for the Spraberry Formation in the Permian Basin was used to build a hydraulic fracture and reservoir simulation model.
Different field development strategies were studied. Models representing a traditional parent-child scenario with five parent wells completed and produced one year before four infill child wells and a traditional parent-child scenario with five parent wells completed and produced five years before four infill child wells are compared. In these cases, a geomechanical finite-element model (FEM) was used to quantify the changes to the magnitude and azimuth of the in situ stresses from the various reservoir depletion scenarios. Next, a cube model with nine horizontal wells completed and produced simultaneously was analyzed. These three scenarios were expanded to include 19 horizontal wells with the same methodology.
This study aims to help operators in the Permian Basin, as well as in other unconventional reservoirs to understand how different field development strategies affect ultimate hydrocarbon recovery and net present value.
Use of Big Data and Machine Learning to Optimise Operational Performance and Drill Bit Design
Authors: Cornel Simon , Vazquez Gonzalo 
Published: November 2020

Abstract

Objectives / Scope
Time savings and bit longevity is one of the major challenges in Coal Seam Gas unconventional fields Onshore Queensland. Maximize rate of penetration based on best drilling parameter was the key target to tackle these issues. The dataset was accumulated from more than 80 bits run. A supervised machine-learning algorithm was used to classify drilling parameters that increase ROP and bit endurance. To achieve this goal, our focus was on a) optimizing PDC bit design, drilling hydraulics and b) developing a Drillers Roadmap to ensure optimal parameters were run in the field.

Methods, Procedures, Process
Operator and Service Company worked closely to select two PDC frames and benchmark performance between them. A novel approach was then taken to capture the rig sensors data. These steps were as follows: Capture big data using sensors integrated on the rig to produce drilling parameters data sets. An algorithm to clean large data sets with the use of Python Pandas libraries was created. Using supervised machine learning and clustering the data to identify which drilling parameters gave the best results. Process the data set using visualization tools to develop heat maps vs depth for each of the drilling parameters (WOB, RPM, flow rates and Torque). Create a Drillers Roadmap identify drilling parameter trends by depth and provide a "sweet spot" based on machine learning for fine tuning operational window.

Results, Observations, Conclusions
A formal process for optimizing performance was developed. After 6 wells, optimal operating ranges were identified. This process leads the driller to respond to changes in formation and deliver more consistent drilling performance with excellent results. ROP increased from 50m/hr to 150m/hr, driven by improved ROP in the lower section of the well. Time saving of more than 150hrs for the drilling campiagn. Refine PDC bit designs based on optimized parameters, therefore improving bit life through cutter and hydraulic enhancements.

Novel / Additive information
The innovation of this methodology is to gather drilling information, cleaning the data with Python using Pandas library thus making this process more efficient. After the data files were clean and QAQC, a supervised machine Learning algorithm was used to optimize ROP's by taking drilling datasets and fine-tuning the parameters (WOB, RPM, Flow rate) using drilling dynamics (vibration) and MSE models. Finally, innovative visualizations (heat maps) were produced in order to give a clear insight of the drilling information gathered by the rig sensors to the company men and drillers.
A Digital Core Laboratory Characterising Oil Water Transition Zones
Authors: Morrison Graeme 
Published: November 2020

Abstract
A multi-chamber, finite-difference dynamic model assumed pore-scale reservoir properties and replicated laboratory processes to determine oil-water transition zone characteristics: (1) imbibition residual oil saturation to water flood (distributed as a function of rock properties); (2) imbibition oil-water relative permeability (according to both steady-state and unsteady-state calculations); and, (3) microscopic oil recovery factor as a function of height above free water level. A priori, scaled capillary pressure hysteresis and segregated flow conditions governed pore-scale flow behaviour (initial mobile saturation scaled 0-1) assuming a pipeline network analogue. Although an oil-water system was modelled the process could be extended to other fluid phases.
The dynamic model was divided into five chambers replicating two experiments. Flow between chambers was regulated by transmissibility multipliers (open/shut). Replicating a core displacement experiment, an oil source rock "drained" (flooded) the central core chamber to form the oil-water transition zone; water then imbibed into the core chamber displacing mobile oil into a shallower effluent chamber; capillary pressure hysteresis defined the imbibition residual oil saturation to water flood. In a second experiment, the central core chamber was subdivided into zones, each comprising five model layers, and the imbibition oil-water relative permeability was determined by flooding the quiesced core chamber situated between high/low-pressure chambers (source/sink). Both steady-state and unsteady-state relative permeability were determined across the oil-water transition zone.
The Digital Core Laboratory (DCL) determined the distribution of imbibition residual oil saturation as a function of: wettability assumption; rock properties; and, proximity to the free water level. For a range of wettability assumptions, imbibition oil-water relative permeability was determined across the transition zone, on a zonal basis, from top core chamber (top reservoir) down to the free water level.
Summary of Results: (1) The imbibition residual oil saturation (ISorw) decreased with depth toward the free water level, while the drainage irreducible water saturation (Swir) increased with depth; (2) ISorw was found to be strongly influenced by the wettability assumption, contrasting with the uniformity of Swir; (3) Microscopic oil recovery factor was determined across the transition zone as a function of both proximity to the free water level and the wettability assumption; except for the oil-wet case, microscopic oil recovery factor increased with depth toward the free water level.
The novel approach was the multi-chambered design of the finite difference model forming a Digital Core Laboratory (DCL). Tuning the model to a Special Core Analysis (SCAL) data set, with the objective of filling SCAL data gaps, is expected to be one application.
Edge Computing: A Powerful and Agile Platform for Digital Transformation in Oilfield Management
Authors: Sharma Abhishek , Samuel Prince , Gupta Debashis , Whatley Craig , Agarwal Shubham , Gey Gian-Marcio 
Published: November 2020

Abstract
In Production operations, asset performance depends greatly on maximizing the run life of equipment while reducing the cost of maintenance. Often, E&P operators have a reactive approach to field maintenance resulting in uneccessary downtime in logistics, inventory management, diagnosing the issues, and in taking the recommended actions. This can lead to higher operating costs and non-productive time.
E&P operators are aggressively looking to increase production with operational efficiency gains. In the unconventional fields, a large number of wells have been drilled and put in production with various artificial lift techniques. Proactive well and field production management requires digital enablement of operations, with no data silos and data flowing seamlessly from the subsurface to the hands of the operator. With huge amounts of data being collected, it is imperative to apply data-driven techniques to gain more insights that can be utilized to better manage production. A data-driven approach can provide huge benefits for organisations holding vast amount of reservoir, production, and facilities data. It could provide insights into non-linear multidimensional relationships between parameters so that the field development is better understood and optimized. It could allow companies using a proactive approach towards field operations and equipment maintenance resulting in additional cost savings.
This paper presents case studies in which operators optimized production utilizing edge-driven Industrial Internet of Things (IIoT) solutions. These edge IIoT solutions enable fast-loop control through a combination of physics and data-driven workflows, which empowers the operator to proactively manage their assets and focus attention on potentially problematic wells. The solution’s architectural setup and ability to deliver fast-loop control workflows at the edge enables operators to successfully detect and manage potential issues and ultimately improve well performance. Additionally, this approach reduces the dependency upon domain experts to frequently analyze data. The high-frequency data capturing resulted in predicting equipment performance with confidence and allowing remote well management to reduce health, safety, and environment (HSE) risks while decreasing logistics and maintenance costs.
First Application of New Generation NMR T1-T2 Logging and Interpretation in Unconventional Reservoirs in China
Authors: Zhang Hao , Wang Yue , Fang Tao , Wang Wei , Zhao Xian Ran , Zhao Hai Peng , Wu Jin Long , Wei Guo , Liu Bo 
Published: November 2020

Abstract
The strong domestic need for oil in China requires further exploration in unconventional reservoirs, such as volcanic and shale oil reservoirs. Sweet zone identification is one of the most critical missions in formation evaluation. The complex mineralogy and the low porosity in unconventional reservoirs result in little contrast of resistivities between oil-producing zones and water zones. Reducing uncertainties affecting the type of fluid in the reservoir and its potential movability is key to defining the optimal landing zones for future horizontal drains.
In conventional reservoirs, nuclear magnetic resonance (NMR) logging based on cutoff analysis is the optimum choice for evaluating the porosity and pore geometry of hydrocarbon bearing reservoirs. However, the routine single-dimension T2 measurement is not sufficient for fluid typing in unconventional reservoirs because of the overlap of signals of various fluids in the T2 domain. This contribution presents case studies from a new generation NMR tool providing continuous 2D T1-T2 measurements. The T1-T2 measurements enable separation and quantification of different fluids in the pores. Fluid typing can be done by integration of other wireline logs such as spectroscopy and dielectric.
Case studies are presented from volcanic and shale oil reservoirs in Xinjiang Oilfield of PetroChina. In the volcanic reservoir case, dedicated 2D data analytics technic is used to extract relevant signals from 2D NMR T1-T2 measurements. An integrated workflow combining wireline borehole electrical image, nuclear spectroscopy, dielectric dispersion and NMR T1-T2 gives an insight into the fluid composition in the pore system. Besides accurate measurements of lithology independent porosity and pore geometry from NMR, T1-T2 specific measurement of clay bound water volume and total water volume match well with the same measurements estimated from nuclear spectroscopy and dielectric dispersion logs. It lowers the uncertainties on fluid types in the volcanic reservoir where resistivities fail to differentiate wet zones and oil zones. In the shale oil reservoir case, a reservoir producibility index (RPI) derived from the integration of NMR T1-T2, wireline borehole electrical image, and nuclear spectroscopy is shown to be quite efficient in sweet zone identification and ranking.
This paper discusses a novel application of a new generation NMR T1-T2 logging method in unconventional reservoirs, which helps the operator ascertain the potential of these reservoirs. The oil zones identified by the new method have promising oil productions. The workflow illustrated in the two case studies can be applied and extended to other unconventional plays in China.
Digital Documentation and Data Management for Offshore Drilling
Authors: Cheng Zhong , Xu Rongqiang , Yu Xiaolong , Hao Zhouzheng , Ding Xiangxiang , Li Man , Li Mingming , Li Tiantai , Gao Jiaxuan 
Published: November 2020

Abstract
Upstream Oil & Gas industry recognizes that there are significant gains to be had by the implementation of new digital technologies. For offshore exploration and development, the goal is to bring together all domains, all data, and all engineering requirements in a seamlessly interconnected solution. The industry is putting significant efforts into using instrumentation and software to optimize operations in all domains for exploration and production (E&P) to move towards the digital oil field of the future. an innovative digital solution has been designed and implemented to cover all different aspects of the well planning and engineering workflows, delivering a step change in terms of capabilities and efficiency.
As part of this transformation process, CNOOC have implemented integrated data management project of geological engineering for covering all different aspects of the well engineering workflows, delivering a step change in terms of capabilities and efficiency. The objective is to provide a continuous improvement platform to users for:
Digitalization can reduce the time spent with daily documentation and simultaneously increase the quality by removing an error prone way of work.
Technological solution enabling real-time data transmission from all rigs to CNOOC onshore headquarters and enabling real-time visualizations of the drilling data. This includes workload, number of needed rigs, daily performance, key performance indicators and even operation time forecasts based on real data.
Engineering solution to transform expert experience and accident cases into information to easily identify the areas of operational improvement allowing to implement specific measures to reduce intangible loss time (ILT) and non-productive time (NPT) which can help in reducing costs.
This project has also provided a real geological drilling environment where high frequency real-time drilling data is utilized along with low frequency daily drilling report data to provide better insights for well planning and generate ideas for improving performance and reducing risk.
This paper presents a full description of a new industry standard digital well construction solution that has the potential to transform the well operation process by providing a step change in collaboration, concurrent engineering, automation, and data analytics. Furthermore, the cloud-deployed solution challenges will be briefly discussed.
The learned lessons and gained experiences from this project construction presented here provide valuable guidance for future demands E&P and digital transformation.
Practical Prediction of Asphaltene Mitigation Based on Dynamic Asphaltene Inhibitor Test DAIT
Authors: Takabayashi Katsumo , Iwama Hiroki , Yonebayashi Hideharu 
Published: November 2020

Abstract
Asphaltenes in the production process often cause significant damage to oil production operation through the continuous growth process (precipitation followed by aggregation and finally deposition) of their particles. To mitigate the asphaltene-induced problems, Asphaltene Inhibitor is used to control precipitation and/or deposition of asphaltene particles. In addition to the typical ambient-conditioned Asphaltene Inhibitor-evaluation known as Asphaltene Dispersancy Test, this study applied the Dynamic Asphaltene Inhibitor Test to select candidate Asphaltene Inhibitors at operating conditions. The Dynamic Asphaltene Inhibitor Test used a simple dynamic flow equipment consisting of capillary coil and filter to detect asphaltene precipitation and deposition independently.
Based on Asphaltene Inhibitors experimentally screened through a series of Dynamic Asphaltene Inhibitor Test, four Asphaltene Inhibitor numerical models were reproduced to assess how Asphaltene Inhibitors mitigating asphaltene risks. A numerical model assuming no Asphaltene Inhibitor was built as reference by calibrating with conventional Pressure Volume Temperature (PVT) experimental results and asphaltene onset pressure. Asphaltene Inhibitor models were generated from the reference model on the basis of comprehensively interpreted data containing available Asphaltene Inhibitor characteristic information. In the Asphaltene Inhibitor models, pseudo heavy components of the crude oil used in Dynamic Asphaltene Inhibitor Test were characterized with cubic-plus-association equation of state. The Asphaltene Inhibitor models were validated by comparing with the relative Asphaltene Inhibitor's effectiveness in experimental evaluation. To understand transition from Asphaltene Precipitation Envelopes to Asphaltene Deposition Envelope, the findings of deposition-information from the Dynamic Asphaltene Inhibitor Test were incorporated into the Asphaltene Inhibitor models. To be more specific, the deposition behavior was estimated on thermodynamic plot by assuming the Dynamic Asphaltene Inhibitor Test condition as pseudo onset condition. The Asphaltene Precipitation Envelopes on thermodynamic plots revealed causing asphaltene precipitation possibly in part of the operation conditions even using Asphaltene Inhibitor. On the other hand, Asphaltene Deposition Envelope suggested that Asphaltene Inhibitor application could reduce asphaltene deposition over a wide range of the operation condition. The upper-boundary locations of Asphaltene Precipitation Envelopes and Asphaltene Deposition Envelope were apart from each other. The fact revealed that Asphaltene Inhibitor could delay asphaltene aggregation and deposition after precipitation. This paper describes a more realistic Asphaltene Inhibitor modeling method based on the Dynamic Asphaltene Inhibitor Test measurement results.