bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring Water Saturation Changes Using New Seismic Attribute in a 4D Seismic Study: An Example in Malaysian Field
Authors: Rezaei Shiba , Ghosh Deva Prasad , Babasafari Amir Abbas , Hermana Maman , Sambo Chico 
Published: November 2020

Abstract
4D seismic (time-lapse) is being used by oil and gas companies as a great tool for improving reservoir management. This technique is consist of repeated 3D seismic surveys captured at different times over same area. 4D seismic technology provides valuable information regarding spatial fluid movements which helps to overcome the challenges that the traditional surveillance methods are unable to solve and in addition it increases the ability of imaging fluid movement between wells after production and injection activities. This paper therefore focuses on 4D seismic method to estimate fluid saturation changes in a producing hydrocarbon reservoir after six years of production and water injection activities. The feasibility of new seismic attributes known as SQp and SQs (lithology and fluid indicator respectively) as a reservoir monitoring and management tool is analyzed and presented which shows the ability of SQp and SQs attributes to detect reservoir fluid saturation changes due to production and water injection activity in the field. To reach our objective, first deterministic seismic inversion was performed to extract seismic properties including Vp, Vs and Density for both base and monitor surveys. Base and monitor surveys were acquired in 1996 and 2006, respectively. Then the inversion results are being used to estimate SQp and SQs attributes volumes. In the final step estimated SQp and SQs attributes are used as an input in neural networks modelling for water saturation prediction. Based on the result of estimated water saturation and seismic attributes the fluid saturation changes was mapped, monitored and compared.
Advances Towards a Comprehensive Model of Laser-Rock Interaction
Authors: San Roman Alerigi Damian Pablo , Batarseh Sameeh Issa , Assiri Wisam 
Published: November 2020

Abstract
Laser applications provide unique advantages to applications in discovery, recovery, and production of hydrocarbons. A comprehensive numerical model would enable prediction, optimization, increase efficiency, enhance control, and further innovation. This work reviews the modeling methods, discusses key variables and physics, presents results, and introduces innovative solutions that make use of machine learning and artificial intelligence to solve an inherently multi-scale and multi-physics problem.
Two possible methods have been explored to model laser-rock interaction: mechanistic and statistical — the former uses as a set of coupled partial differential equations that adequately describe the physics involved. The statistical method uses advanced statistical analysis and supervised-learning to elucidate relations between the experimental settings and observations. The full-physics or mechanistic model was developed using finite-element and finite-difference methods; it incorporates coupled solvers for electromagnetic, thermodynamics, and geomechanics. The statistical model uses advanced statistical analysis and machine learning to characterize the dynamics and build a prediction algorithm.
A numerical model of laser-rock interaction must comprise physical dynamics that span over different time and spatial scales. When a laser beam impinges on a rock, a portion of the incident energy is absorbed as thermal energy, and a thermal gradient is created. The result is a distribution of physical and chemical changes such as spallation, melting, dissociation, calcination, or vaporization. The full-physical model can adequately capture the transient process; however, it requires a functional characterization of the dynamic rock properties, environmental conditions, and laser parameters. The statistical approach provides a prediction of the overall outcome of the process departing as a function of known input parameters, yet its precision depends on the availability of experimental data (outcomes and conditions). Key parameters are identified using statistical analysis. The modeling results agreed with experimental tests. Further, they evince that thermal properties and geomechanical stresses configuration have a significant impact on the process’ outcome. These methods can optimize and predict the interaction for multiple applications, ranging from heat treatment to stimulation.
Subsurface laser operations could provide the next generation of stimulation and workover tools for Oil and Gas. Numerical models of laser-rock interaction are essential to predict, optimize, adapt, and evaluate subsurface laser applications during development, test, and operation. This work provides a basis for the development of future numerical models and enables the next generation of subsurface photonic tools.
AutoEncoder Neural Network Application for Coherent Noise Attenuation in High Frequency Shallow Marine Seismic Data
Authors: Hamidi Rosita , Latif Abdul Halim , Lee Wei Yi 
Published: November 2020

Abstract
Conventional noise attenuation methods involve transforming noisy data into a filter domain where noise and signal can be separated. Deleting the noise components and transforming back the data into original domain, the filtered data is achieved. Coefficients representing the noise in the filter domain are selected by thresholding or manually which can result in a time-consuming process and also introduce error to what should be considered as noise energy. In this study, a model is developed using Deep Neural Network with AutoEncoder architecture to select the noise energy automatically in the Frequency-Wavenumber domain. The objective is to train a model that can attenuate coherent noise with certain isolated frequencies and varying amplitudes while preserving all reflections (weak and strong). The network is only trained on synthetic data; but its performance is evaluated on real high frequency marine data. The synthetic data have very simple structure of high frequency reflections contaminated with sinusoidal noise; outstanding performance of the proposed method on real data, however, shows the exceptional capability of the Deep Neural Network based filters.
End to End Real Time Data Cleansing, Standardisation and Reliability Towards a Smooth Digital Oil Field Deployment
Authors: Aissani Abdelmadjid , Tan Jun Yi Edwin , Thamilyanan Thivyashini 
Published: November 2020

Abstract
In order to remain competitive, digital solution in upstream oil and gas industry "is not one of the option, it's the only option". Nowadays, many companies have embarked on the digital transformation journey, and striving to become a data-driven organization. Service providers and consultancy companies constantly propose their latest technology such as Advanced Analytics, Big Data and digital oil field (DOF), however, the success of these digital solution relies heavily on data quality, where companies have always struggled to ensure a high level of data quality and make their readiness towards digital transformation questionable.
Real time data has become an essential tool in production surveillance, monitoring and asset optimization, it provides the engineers a breadth of view on the operational behavior of their wells and facilities. However, multiple issues arise that hinders the right data before flowing into the system such as instrument failure, bad data, wrong mapping, duplication, misleading descriptions and inconsistence in naming convention. These discrepancies will lead to low quality data being fed into the system and ultimately affect the efficiency of any deployed digital solution. Therefore, for any organization to leverage the value of their data and to unlock new opportunities the real time data needs to be well managed, governed and provided in high quality.
In this paper, the approach of real time data cleansing and standardization will be addressed in detail from end to end based on an initiative conducted in one of the signature field in Peninsular Malaysia. A thorough cleansing was done from the verification of instruments onsite, P&ID to all sub systems (servers) and end up with reporting all the finding (Completeness, Validity, Uniqueness, Consistency, Timeliness, Accuracy…). Therefore, provide a real time master data management, this helped in a good understanding of the real time data flow and identifying the responsible parties.
This has resulted in the development of a robust workflow accompanied by a clear RACI that can be used during the replication in other fields as well as future developments. Author believes that other operators may benefit from the proposed cleaning strategy and standardization logics to prepare for digital transformation.
Successful Application of an Integrated Fluid and Cake–Breaking System on Three Challenging Extended–Reach Wells Drilling Pinnacle Carbonate Reservoir
Authors: Correa Karen , Rincon Marco , Tieng-Soon Heu , Runtuwene Michael 
Published: November 2020

Abstract
A major operator in Malaysia planned to drill into a 6,000-ft horizontal thin-oil rim. The three-well campaign posed very challenging well deliveries for the operator due to potential losses to faults and karst zones, high uncertainty of reservoir productivity, long horizontal laterals, and openhole completions containing an Autonomous Inflow Control Device (AICD) with a low solids tolerance. An optimized drill-in fluid (DIF) and delayed filter-cake breaker were designed and tested to address the following challenges: reduced filtrate invasion, a targeted filter-cake breaking delay time, and high well production rates. In addition, there was a need for a mitigation strategy for total losses and the resulting impossibility of placing the delayed breaker across the entire reservoir section.
A customized NaCl-based 9.2 lb/gal DIF with an optimally sized ground marble bridging package was designed to facilitate filter-cake removal using a compatible delayed acid-release cake breaker. This was achieved through the addition of special additives in the DIF to increase the acid solubility of the filter cake. The DIF also included special additives proven to facilitate liftoff of filter cake at relatively low differential pressure, and for enhanced thermal stability of the polymers used.
Testing confirmed that the DIF filter cake was efficiently removed by the breaker within the customized delay time. Corrosion testing of the metallurgy used in the completion equipment demonstrated low corrosion rates at the expected 265°F bottomhole temperature (BHT) by both DIF and breaker systems.
Laboratory test results confirmed a high return permeability of 97.5% using the integrated DIF and breaker system. Three wells were drilled in the thin-oil rim reservoir and were completed using this system without losses. A relatively high ROP of 90 to 175 feet per hour without differential sticking issues maximized drilling efficiency and reduced the overall drilling costs. DIF density and properties were controlled through a dump-and-dilute technique. The wells were drilled to TD, and the completions run without incident. After the breaker was spotted, sufficient delay time was achieved with which to remove the wash pipe prior to closing the reservoir isolation valve. Approximately two months after spotting the first breaker, the wells were brought online, and the actual gas/oil production rates of the three wells were 50% above expectation.
This paper presents the design and successful application of an integrated DIF/breaker system for drilling and completing three of the most complex extended-reach wells drilled by the operator in Malaysia. The complementary designs of the DIF and breaker system components contributed to the successful drilling and production results of these three challenging wells.
Qualitative Root Cause Assessment Approach to Quantify the Impact of Flowline Internal Coating Disbondment
Authors: Lai Han-Lyn , Zakaria Mohd Hafiz , Ho Harry 
Published: November 2020

Abstract

Objective and Scope (25-74 words)
During High Speed Flushing (HSF) of two water injection flowlines (WIFL) located at offshore Sabah, Malaysia at water speed of 3.0m/s, internal Fusion Bonded Epoxy (FBE) coating flakes were observed being ejected from end of flowline by ROV. There were concerns that the dis-bonded FBE flakes would block the wells during operation causing a reduction in the water injection performance. The objective of this qualitative root cause analysis is to determine the likelihood of further internal FBE coating disbondment during operation and assess whether the water injection flowlines internal FBE coating failure is localize or systemic. Systemic failure is defined as the location of the internal FBE coating disbondment has no specific pattern and is randomly distributed throughout the entire flowlines. Localize failure is defined as the failure pattern is discernible and limited to a few specific linepipe along the flowlines.

Methods and procedures, process (75-100 words)
The qualitative roots cause assessment of the internal FBE coating disbondment consists of the following work streams:

Results, observations, conclusions (100-200 words)
In conclusion, there is no evidence to suggest that the FBE disbondment in the water injection flowlines was a systemic failure or that further application of flow would cause more coating to detach from the flowlines. It is deemed that the internal FBE disbondment is high likely localized based on the following observations:

New/novel information (25-75 words)
The water injection flowlines had started its operation since September 2015 and the wells performance is closely monitored by WRFM team. To date there is no decline in water injectivity being observed or reported for all water injection wells. Hence, the qualitative root cause analysis approach employed by the project team is deemed a workable analysis method in concluding the water injection flowlines internal FBE coating failure is localize and low likelihood of further disbondment during operation.
Practical Application of Machine Learning to Geotechnical Correlations
Authors: Tho Kee Kiat , Chan Nancy 
Published: November 2020

Abstract
Correlations are prevalent in the geotechnical engineering practice. This paper presents a practical machine learning approach for establishing improved geotechnical correlations. In the realm of machine learning, multiple input variables of significance can be readily and coherently incorporated into the model. This approach is illustrated in the context of correlation between undrained shear strength and the combination of depth, water content, liquid limit and plastic limit. The methodology is presented in a general form to facilitate adaptation to other geotechnical correlations.
The machine learning model is created in TensorFlow which is an open source machine learning platform. A dataset which consists of 1013 groups of undrained shear strength from undrained-unconsolidated triaxial test, depth of sample, water content, liquid limit and plastic limit data are compiled. 70 % of the randomised dataset are used for training and validating the model while the remaining 30 % are used to test the performance of the model. The machine learning model consists of an artificial neural network model with one input layer, four hidden layers and one output layer. The training takes several minutes on a laptop equipped with a Graphics Processing Unit.
The classical approach would have been to correlate the undrained shear strength to liquidity index which is a combined parameter computed from water content, liquid limit and plastic limit. By comparing the predictions of the machine learning model on the test dataset against those computed based on dataset-specific correlation between undrained shear strength and liquidity index, it is evident that a significantly improved correlation is obtained using the machine learning approach.
Due to the intricacy of multi-dimensional regression analysis, classical geotechnical correlations are typically determined by method of curve-fitting with just a single independent variable. Soil, by nature, is a complex material that is characterised by multiple index properties. Therefore, it is intuitive that geotechnical correlations can be improved by incorporating multiple classification properties or measurements. The machine learning approach described in this paper, which is implemented using an open source platform and readily accessible to industry practitioners, alleviates the legacy limitation of a single independent variable leading to improved geotechnical correlations. More importantly, this machine learning approach fits in perfectly with the digitalisation initiative increasing embraced by the oil and gas industry to improve on safety, efficiency and profitability.
Breakthrough in Scale Inhibitor Chemical Selection through Digital Application
Authors: Collins Jonathan Anthony , Dunsmore Braden , Grutters Mark , Howard Lee , Shintre Nitin , Selvam Kathiravan 
Published: November 2020

Abstract
Production chemicals are essential to the operation of oil and gas production systems – but chemicals such as scale inhibitors are complex, with different chemistries providing protection for different types of situations, depending on system conditions. To date, the standard industry process has depended on experience, laboratory testing and field trials.
We are introducing a first of-its kind method of scale inhibitor selection which takes advantage of digital capabilities, rich performance data and our deep chemicals expertise to eliminate much – and sometimes all – of the laboratory work.
In specific, this process eliminates the time needed for short-listing and testing scale inhibitor chemistries in the laboratory. We have instead applied digital analytics to our extensive historical data library, making it possible to recommend the appropriate chemistry with reduced – or even completely without – laboratory testing. Comparing system conditions to historical laboratory data, we can generate relevance factors that relate to the conditions of the system of interest. If sufficient information is available in the database, the analytics can also predict the results that would be expected in a given laboratory test. In addition, data on where each scale inhibitor has been applied is stored in the database, giving a link to field scenarios and case histories, thereby correlating laboratory and field performance.
Laboratory tests confirm that the use of this new digital application results in much quicker (same day) and more accurate product recommendations, with statistical qualification. It allows the user to generate faster recommendations, optimize dosages, generate tailored products, and avoid delays from shipping samples around the world. Ultimately, it provides more statistically relevant information on which to base field decisions, minimizing risk for the operator, as well as much quicker and optimal product recommendations.
We believe that this revolutionary scale inhibitor chemical selection tool is the first of its kind to incorporate such an extensive database of historical test data with digital correlation to existing field use information. We believe it is the future for designing the optimum scale treatment program for oil and gas fields.
Unconventional Mud Design to Address Narrow Pressure Window and High Overbalance HPHT Offshore Well
Authors: Razak M. Solehuddin , Devadass Lingges 
Published: November 2020

Abstract
Alpha 1 was recognized as the highest overbalanced well drilled in operator's history. A comprehensively designed superior mud system has enabled the well to be drilled successfully, meeting its objectives of producing shallow and intermediate intervals and able to appraise deeper 8-1/2" HPHT section. The HPHT section exhibits a marked pressure spike to pore pressure up to about 14,000psi with 310°F bottom hole temperature. The formation prognoses are very much uncertain due to limited data available from offsets. This considerable HPHT drilling challenges are further compounded by the high overbalance pressure - approximately 6,800psi observed while drilling with 18.4ppg mud weight.
Drilling mud designed for high overbalanced pressure well requires special attention particularly on the HPHT filtration control and mud rheology. In deeper section where wellbore pressure control is critical with approximately 1.5ppg drilling margin, the mud formulation and rheology was optimized in minimizing ECD. The components of the mud were heavily scrutinized to minimize risk of wellbore losses and differential sticking. Low fluid invasion system was introduced to provide wellbore shielding mechanics. Operator has made initiatives of conducting unconventional laboratory works in assuring the technical performance. Cost reduction initiative was also made by optimizing mud design with surplus mud and chemical inventory. These were incorporated into the mud design as to have low cost and fit for purpose mud formulation.
In actual operation, the mud rheology and ECD were deviated from the plan. Both major deviations demanded several adjustments on the mud formulation. Several lab tests were conducted and unconventional mud apparatus were brought offshore for further fine tuning on mud formulation. The 8-1/2" HPHT hole section was successfully drilled with no issue pertaining to losses, drag during tripping and differential sticking. The best practices and lessons learnt replicated from few HPHT wells drilled previously, in combination with the close collaboration on laboratory works with contractors has contributed greatly to the success of the well.
Offshore Wind Energy in Asia: Technical Challenges and Opportunities
Authors: Lau Hon Chung 
Published: November 2020

Abstract
Studies show that coastal regions of the East China Sea, Japan Sea, Philippine Sea and parts of the South China Sea have adequate wind speeds for offshore wind energy while coastal regions in equatorial Asia do not. Inexperience in installation, operation and maintenance (O&M) and the high levelized cost of electricity (LCOE) are major challenges in Asia’s roadmap to implement offshore wind farms. Several technologies in wind turbine design that will aid implementing offshore wind farms in Asia have been identified. They are weight reduction, increased reliability to reduce O&M, typhoon resistant design, integrating substructure into wind turbine design, electrifying oil and gas platforms by wind turbines, and combining multiple renewable energy systems in a single floating platform.