bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterisation of Paraffin Crystals Aggregation Behaviour in Shearing Fields Using Fractal Dimensional Analysis
Authors: Ni Guangyuan , Wang Zhihua , Yang Heng , Li Jiexun , Zhao Xinming , Liu Yang 
Published: October 2019

Abstract
The paraffin crystals can aggregate and precipitate when the oil temperature declines below the wax appearance temperature (WAT) of the waxy crude oil. However, the aggregation behaviour of paraffin crystals during the process of crude oil production and transportation is not well revealed. This study concentrates on using fractal dimensional analysis to elucidate the aggregation behaviour of paraffin crystals in shearing fields. A set of microscopic experiments was recently performed to capture the basic information of the paraffin crystal images in different conditions. A methodology for characterizing paraffin crystal aggregation was then developed, and the blanket algorithm was introduced to compute the fractal dimension of aggregated paraffin crystals. The results indicate that the aggregation behaviour of paraffin crystals is closely related to temperature and shearing experienced by the waxy crude oil. The aggregation behaviour would be intensified with the reduction of temperature and the decrease of shearing effect, and it appears to be a wider fractal dimension distribution at lower temperature when the same shear rate range is employed. The lower fractal dimensions are at high temperature and strong shear action, the weaker nonlinear characteristics of paraffin crystals aggregation structure are, and thus the potential paraffin deposition would be inhibited during waxy crude oil production and transportation. The methodology developed in this study is beneficial to optimize the flow assurance operations in single-phase and multiphase pipeline transportation for waxy crude oils, and the results are expected to lead to a better understanding of paraffin crystallization and deposition mechanism.
Determination of Sand Production Intervals in Unconsolidated Sandstone Reservoirs Using Spectral Acoustic Logging
Authors: Aslanyan Irina , Maslennikova Yulia , Minakhmetova Roza , Aristov Sergey , Sungatullin Lenar , Giniyatullin Artur 
Published: October 2019

Abstract
Solid particles (sand) production in oil and gas wells can significantly affect well productivity. The negative effect can be caused by sanding up of the perforations, tubing and downhole equipment, and abrasive wear of the well completion and surface equipment. The near-wellbore zone can also be damaged. Considering this, it is critically important to be able to identify sand production intervals for the purposes of ensuring sand control and preventing the negative impact of sand production.
The paper presents a new method of locating sand production intervals using spectral acoustic logging tools, which consists of detecting signals generated by solid particles being transported by fluid flow and hitting the acoustic logging tool housing. The next generation spectral acoustic logging technology was used to identify reservoir intervals producing solid particles. The developed method was tested and utilised in a deviated well of one of the large gas fields in South East Asia, where sand production was a serious problem. Well logging operations, including spectral acoustic logging, were performed in two different production regimes. This approach not only demonstrated a good data repeatability, but also helped determine the optimum production regime of the well with sand production being under control. The acoustic logging data was processed by an adaptive recognition algorithm to identify acoustic signals generated by solid particles striking against the tool housing. The method had previously been tested in the lab.
The proposed method of identifying sand production intervals in producing wells makes it possible to locate sand production zones and define a production profile in one logging run. The obtained information was used in planning a remedial job with the objective of isolating sand production intervals in the surveyed well.
A Novel Formula for Estimating Oil Compressibility Below Bubble Point Pressure Using Group Method of Data Handling: A Comparative Approach
Authors: Ayoub Mohammed. A. , Almansour Abdullah. O. , Hassan Anas. M. 
Published: October 2019

Abstract
Oil compressibility (co) plays a vital role in vast aspects ranging from upstream to downstream. For reservoir with pressure below bubble point, the effect of co to the fluid flow is insignificant as it is overshadowed by the presence of large gas compressibility (cg). This study aims to increase the range of applicability and accuracy of the formula used for estimating the co by eliminating the limitations of other existing correlations.
A new formula for the estimation of oil compressibility below bubble point pressure is devised using Group Method Data Handling (GMDH). The approach is a combination of neural networks and some high-level statistical methods which rely on generating simple relations among the input parameters and the dependent parameter. The relations then result in eliminating some parameters with low impact on the output. A series of consecutive layers with the link is generated, and polynomial terms are created. A total number of 322 data points were collected from different sources from literature.
Systematic trend analysis has been conducted to verify that the proposed GMDH model honours the exact physical behaviour. The new proposed model found to follow the correct trend, which implies its soundness. Besides, a comparative study was carried out using the best available correlations to confirm the significance of the results of the oil compressibility prediction using GMDH. Different statistical analyses have been conducted to verify the robustness of the newly developed model. The statistical analyses showed a positive outcome whereby the proposed model obtained the lowest average absolute percentage relative error of 5.17% and the highest correlation coefficient of 96.8%. The best model tested among the other models has five input parameters and an average absolute percentage relative error of 10.955% and a correlation coefficient of 95.6%.
The new approach managed to reduce the curse of dimensionality as four input parameters have found to have a strong dependency on co (solution gas-oil ratio, oil density, reservoir temperature, and reservoir pressure). The new proposed model overcome the limitations described by the locality of some correlations as they depend on data from specific locations.
Data Conditioning and Suitable Data Tolerance for Automated Production Workflows in a Digital Oilfield
Authors: Kok Jason Chin , Saiful Mulok Nurul Nadia 
Published: October 2019

Abstract

Objectives/Scope
In today's Oil and Gas Industry, as we evolve more towards smart fields and integrated operations, the term digital oilfield is being used more often. Many companies implement smart fields where transmitters and gauges are installed in the field along with a Supervisory Control and Data Acquisition (SCADA) system. One common threat faced in a digital oilfield setup is the failure of accurate data transmission. This includes no data transmitted, erroneous/wrong data transmitted, error in the calibration of the field devices, network interruptions, failure of SCADA system etc.

Methods, Procedures, Process
This paper describes the development of a methodology used to overcome the issues of bad data from the field with the usage of data conditioning and also the suitable data tolerance which can be accepted before the system fails. This method uses smart rules and automated data conditioning which involves historical data, data from nearby wells and results from well modeling workflows. When applied in the field, it ensures that the system runs even when not all the required data from the field is acquired for successful workflow execution.

Results, Observations, Conclusions
Implementing data conditioning rules described in the paper improved the accuracy of the workflow from 71% to 98% which represents a huge improvement. The solution was applied in an offshore field. The automated workflows involved in this solution was well status identification, real time production surveillance, real time gas lift optimization, theoretical production rates from well models, well test validation and platform monitoring. As more data becomes unreliable and missing, the confidence level of the results of the workflow also decreases to a certain point where too many required data points are missing or not available and the system would send an alarm and flag the results of the workflow due to unreliable data transmission. This scenario will be seen if there is a complete failure of the SCADA system or severe network interruptions for an extended period of time. The solution and data conditioning methods applied managed to increase production by reducing the amount of time needed to identify wells which quit offshore which reduces deferred production.

Novel/Additive Information
The paper provides a quantitative assessment of the benefits realized by applying the data conditioning and using suitable data tolerance when running workflows used for production surveillance and optimization in a digital oilfield. As we try to achieve higher production rates and greater recovery from the field, more high frequency data is required and the methods described in this paper can be applied to other fields to solve the missing and erroneous data transmission problems.
Utilizing Petrophysical Facies Model in Multi-Layer Pressure Transient Analysis: A Novel Approach
Authors: Kumar Arvind , Ojha Keka , Wydiabhakti Tety Benedicta , Nahar Siddhartha 
Published: October 2019

Abstract
Petrophysical facies modeling plays a key role in reservoir characterization at all levels. At a well level it helps to delineate the layers on basis of certain similar rock physics characteristics, which further can be used in reservoir engineering computations that include layer wise input of properties. At a field level petrophysical facies helps in mapping of reservoir units in a multi-well scenario.
Pressure transient tests are performed to determine the reservoir properties like horizontal permeability (Kh), vertical permeability (Kv), skin, knowledge of reservoir boundaries and understanding the reservoir structure up to a level etc. All these are used in the field development planning (FDP).
Conventionally, in a pressure transient interpretation a reservoir layer is taken as homogenous, i.e., the reservoir properties are taken uniform across the thickness of sand unit. In highly heterogenous reservoirs, this approach may lead to under-estimation or over-estimation of permeabilities, since a homogenous layer doesn't consider the vertical heterogeneity within the layer. Hence, to address the vertical heterogeneity, multi-layer reservoir model is used in pressure transient interpretations. Each of these layers can be treated as a petrophysical facies.
This paper discusses various ways of petrophysical facies modeling and showcases the usage of these layered reservoir models in pressure transient interpretations. The results from both conventional as well as multi-layered model are compared in different type of reservoir sands.
It is observed that a multi-layer reservoir model gives better results for vertical and horizontal permeabilities in a vertically heterogenous reservoir. The degree of layer division defines the vertical resolution or refinement of permeability values. In a homogenous sand unit, the conventional model can be used up to a certain degree of accuracy.
Revolutionary PDC Cutter Design Help Improve Bit Performance in Malay Basin
Authors: Shahrul Amar Muhammad Shafiq , Seow Beng Chai , Za'ba Muhammad afiq , A Razak M Khairol , W Aziz W Nor 
Published: October 2019

Abstract
This paper describes the application of using new 3D shape PDC cutters in XX development wells in offshore Sarawak. Based on historical drilling data from offset wells around the XX Field, multiple bit trips were required on each section from 17-1/2" until 8-1/2" holes. Various bit manufacturers had come up with several bit designs to drill this field. Post-run bit dull reports showing that those bits were worn out due to formation related issues which were suspected due to interbedded soft shale and abrasive sandstone.
One of the greatest challenges that any PDC bit manufacturer encounter today is the combination of crushing (roller cone) and shearing mechanism (PDC) into drilling challenging formation with single drilling trip. Current PDC application into hard rock drilling, where impact damage, heat damage and abrasive wear of PDC cutters limit its performance. XX Field formation profile with interbedded soft shale and abrasive sand make it impossible to predict on ideal bit selection with combining PDC and Roller Cone runs together effectively.
The challenges have led to the development of a new 3D shape PDC cutter tested in XX Field development wells successfully. The client saved multiple trips of PDC bit runs and drilled 50% farther, double rate of penetration (ROP) than the best PDC bit run in the offset wells. The revolutionary bit cutter design works well regardless of any hole size and BHA configuration. This study will present an analysis of the drilling data from the field and provide a detailed description of the bits and cutter design achievement by comparing the performance of both roller-cone and conventional PDC.
The 3D PDC cutter design has generated a stable rotation and reduced vibrations throughout drilling which resulted in improved ROP while extending the bit life at the same time. The new ground-breaking bit cutter design has provided a viable solution to drilling interbedded soft shale, abrasive sandstone with unexpected hard stringers.
Data-Driven Diagnosis for Artificial Lift Pump's Failures
Authors: Ounsakul Thanawit , Rittirong Ake , Kreethapon Thanawee , Toempromraj Wararit , Wejwittayaklung Kittipat , Rangsriwong Phattarakorn 
Published: October 2019

Abstract
Beam pump and ESP are common artificial lift techniques in pumping systems. They are widely used as primary oil recovery methods, but system failures lead to production deferments and increases in operating expenses. Employing decades of our field data, promising data science techniques are discussed here to analyze the factors governing failures in both beam pump and ESP approaches. These data are then applied with machine learning models to predict service life, failure mechanism performance, and production deferments.
The data analytics process begins with data preparation. Field data were extracted, transformed and loaded into a data warehouse for further processing. These data were categorized by failure information, pump configuration, wellbore geometry, and production information. The significance of each parameter causing pump failures was derived using a process called "Attribute Forward Selection (AFS)." Then several machine learning algorithms were implemented and compared with to determine the most appropriate model to predict pump service life. More suitable pump configurations to improve pump service life were conceptually recommended based on the analysis.
Differences in parameter significance was identified by attribute forward selection, and is displayed in a heat map. It was seen that the use of beam pumps in highly tortuous wells received the number one ranking as the main cause of failures whereas sand production was revealed as the most significant parameter relating to ESP failures. Correlations for these parameters were mapped by machine learning algorithms, resulting in multivariate failure prediction models (i.e. involving more than one parameter at a time) to predict the service life of beam pump and ESP systems. For both artificial lift systems, the models with the best correlation found thus far are based on a neural network, which resulted in the highest R-squared values when compared to other techniques. This neural network model was validated with the actual information, and the outcomes using this model are presented via a scatter plot in this paper. The plot shows that the prediction for ESP forms a trend around the theoretical best match line. In contrast, the prediction for Beam pump still needs improvement, with the data being scattered around the straight line with a unity slope.
Data science is an emerging technology that recently has provided breakthrough results for big data analysis. This paper will demonstrate the application of such discipline to the area of artificial lift. Machine learning is a promising tool which could help improve human understanding of complex problems, and, in this case, could furnish a durable competitive advantage to the oil and gas industry.
Streamlined Application to Integrate, Standardise, and Automate Well Rate Test Data Analysis
Authors: Al-Mubarak Yousef , Al-Mukhtar Mohammed , Al-Hanabi Mohammed , Mehmood Sajid 
Published: October 2019

Abstract
This paper aims to describe an integrated data analysis and validation application for well testing. This is a computer based application set to work with the different types of wells (water injection, oil producers and gas producers). In addition, it features data capturing, validation, analysis and several techniques for data visualizations.
The conventional well testing process goes through: data capturing, loading, calculations, validation and data archiving. This process was handled by multiple isolated systems that required different workflows based on the data sources. In addition, these systems were designed to handle conventional testing methods such as High Pressure Test Trap (HPTT) separator.
With the introduction of continuous real time measurement technology, such as Multi Phase Flow Meter (MPFM), the volume of the data expands tremendously. This imposes the challenge of big data analytics while ensuring the quality and the consistency of the results. This resulted in a demand for a system to streamline, integrate, standardize and automate the process of well testing. The Integrated Well Rate Analysis System (IWRAS), was developed and deployed to overcome the encountered challenges.
This paper describes well test handling in IWRAS with an example of how the application unified the process workflow for oil producers equipped with MPFM. It will also shows the benefits of utilizing this application in handling real time data.
Remedial Sand Control in Mature Fields: Lessons Learned From Thru-Tubing Ceramic Sand Screen Application in Offshore East Malaysia
Authors: Yeap Wei Jian , Idris Khairul Nizam , Sidek Sulaiman , Cheol Hwan Roh , Kamat Dahlila , Tang Catherine , Zimmermann-Ptacek Johanna , Gundemoni Bhargava Ram , Jackson Steven Richard , Barth Peter 
Published: October 2019

Abstract
Formation sand production is one of the major production challenges in most of the mature fields in Malaysia. Often, failure in primary sand control equipment requires the operators to adopt through-tubing sand screens as remedial sand control. Due to the erosion prone nature of the thru-tubing metallic sand screen, operators are forced to impose limitation on the production rate coupled with stringent surface sand monitoring system to avoid surface flow line leakage and loss of primary pressure containment. Therefore, to seek a more robust technology than conventional metallic screens, alternative technology with through-tubing ceramic sand screen (TTCSS) has been considered with the idea of higher durability and resistance against erosion. This paper will discuss the performance and lessons learnt from the application of through-tubing ceramic sand screen throughout several mature fields in Offshore East Malaysia.
Over the past two years, there have been about twenty-five TTCSS installations in Offshore East Malaysia. Seven pre-mature failure cases were observed, where sands were produced to the surface and even caused leaks on the flow line. The average effective production period for all TTCSS across all the fields ranges from 6 to 11 months. Teardown investigations have been conducted to diagnose the failure root cause.
Most failed TTCSS exhibited similar failure patterns at the end caps, which house the spring compensator. Failure to stop the flow through the end caps led to substantial erosion at spring compensator and base pipe, inducing large flow path for sand production to the surface. Other than the design failure, application failure was observed at the ceramic rings due to their brittleness.
Computational fluid dynamic simulation and laboratory testing have been conducted at the higher incremental production rates to support the observations from teardown inspections, refine the hypothesis of failure mechanism and enable an incremental design change to be modified into TTCSS. In order for TTCSS to be one of the competent candidates for remedial sand control, new improved standard design of TTCSS with strengthened end cap area will be studied to prevent similar failure.
Unlocking Well Potential with CO2 Tracer Application in High Deviation Well
Authors: Kueh Jing Zhi , Lim Jit Sen , Claudius Rick Raynold , Huong Chii Seen , Dev Daevin , Thamilarasu Mohana Ramanee , Abdul Wahab Nurul Syafiqa , Hashim Hazlina 
Published: October 2019

Abstract
In offshore environment, it is common for operators to develop the field with high deviation wells, in order to optimize both field development cost and reservoir drainage. Well intervention for high deviation wells can be difficult, as specialized tools are needed for well intervention in high deviation wells. As such, high deviation wells normally have well issues after 5 years of production.
In Field B, 4 high deviation wells were having unhealthy well lifting, well lifting diagnostics and optimization was needed in order to sustain well and field production. CO2 Tracer Survey was selected for well lifting diagnostics, and all wells were found to have some degree of well lifting issue. With the result from CO2 Tracer Survey, gas lift optimization was conducted, and well was restored to its design condition.
Success Story: Offshore Well Services Using Rigless Offshore Scaffolding Pipes Structure in TBA Field Salawati
Authors: Quraishi Rezi , Mubaraq Munajat , Prayogo Yudie , Yogaswara Andri , Hafeez Al , Robani Aris , Prabowo At Budi 
Published: October 2019

Abstract
Offshore TBA field production has going to the marginal phase where to produce need using artificial lift (gas lift). The gas source for the gas lift is the associate gas produced from the existing wells. The TBA field needs additional gas production to make up the Gas Compressor Unit due to production of the associate gas has declined. This additional gas production will be obtained from top layer of perforation zone. The perforation activity cannot be carried out using rig unit and snubbing units due to limited space deck on tripod platform and high cost operations.
To solve the problem, offshore scaffolding pipes structure (rigless) has become an alternative option to complete the job. This alternative will be supporting as static lifting equipment to R/U all units and running all devices at the platform during well service activities such as perforation, bottom hole pressure survey and gas lift valve replacement.
The success story of using offshore scaffolding pipes structure (rigless) with the steps procedure for installation and dismantle will be discussed in this paper. It is expected that the success of using offshore scaffolding for the operation activity will be a new breakthrough in how to create efficiency in the oil and gas industry.