bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating Hole Cleaning Capability of Biodegradable Fiber-Containing Drilling Fluids in Riserless Tophole Drilling
Authors: Aoki Tatsuyoshi , Naganawa Shigemi , Abe Kazunori , Mukai Ryutaro 
Published: November 2020

Abstract
Fiber-containing drilling fluid enhances hole cleaning or cuttings transport efficiency without significantly affecting the rheological properties of the base fluid. Objective of this study is to evaluate the hole cleaning capability of a novel seawater-biodegradable polymer fiber-containing drilling fluid for use in riserless tophole drilling through large-scale cuttings transport experiments.
The Poly(3-HydroxyButyrate-co-3-Hydroxyhexanoate) (PHBH) is 100% bio-based and biodegradable yet resistant to heat and hydrolysis. Dispersibility of the PHBH polymer fiber in base fluids and rheological properties of the fiber-containing fluids were examined in the laboratory. Cuttings transport experiments were then conducted using the large-scale cuttings transport flow loop apparatus for several selected combinations of base fluids and fibers based on the lab test. Cuttings transport behaviors were recorded on a digital video for further image analyses.
The fibers showed good dispersibilities in base fluids with various concentrations of polyanionic cellulose (PAC) polymer and even in water. Adding fibers to base fluids increased viscosities at low shear rate ranges corresponding to 3, 6 and 100 rpm measurements of Fann Model 35 rotational viscometer without significant changes in overall rheological properties, which is expected to considerably improve cuttings transport efficiency. In addition, this positive tendency to cuttings transport efficiency was more significant in relatively low concentrations of PAC. To the video images recorded during the cuttings transport experiments, the particle image velocimetry (PIV) technique was applied to determine the cuttings transport velocities and cuttings concentrations accumulated in the annulus. Adding fibers increased cuttings transport velocities by up to about 1.5 times in vertical annulus. In near-vertical (hole inclination angles up to 30°) annulus, the cuttings were observed to flow as dunes or fluidized beds in which the cuttings re-entrainment from dunes was confirmed to be enhanced particularly at lower fluid flowrates.
Optimizing Hydrocarbons Recovery in a Complex Reservoir with Extremely Uneven and Low-Resolution Sand Distribution
Authors: Ahmed Sheraz , Waqas Ghulam , Sajid Muhammad , Bugti Jahanzeb 
Published: November 2020

Abstract
In the midst of current oil prices, most companies have shifted their focus on cost-constrained policies for hydrocarbon exploitation, thereby making robust subsurface description a real challenge. Developing a reservoir in such scenario is a massive problem. This study presents various engineering algorithms on such a low-resolution and uneven sandstone reservoir, to optimize the development and expected ultimate recovery - while honoring multiple challenges and limitations.
This study focuses on a newly discovered formation, in one of the oldest fields in Pakistan. The productive section of the reservoir comprises of sandstone units in beds up to 40 m thick, separated by mudstone intervals; and is interpreted to be deposited in lower progradational intervals of a delta. To date, three different depositions have been identified with two of them in hydraulic communication and one isolated with faults. Since the seismic data has limitations in its resolution; various engineering analyses were carried out, among which the variations in reservoir pressures, GWC and PVT properties played a major role in the identification of partially drained and isolated prospects.
The first two successful wells in the isolated compartment were high producers. However, sudden water production (from fractures) loaded up these wells with only 45% recovery from the compartment - very low for a weak-water drive dry gas system. The static and dynamic data set also showed the existence of more Hydrocarbons. Thus, several engineering and geological information, including the re-interpreted Seismic and simulation models, were applied to analyze the possibilities of concurrent sweet spots. Finally, after utilizing multiple algorithms & risk calculations, more wells were drilled - leading to the revelations of two more depositions in the reservoir, which were in hydraulic communication with each other but isolated with the first compartment. This added around 60% more reserves to the current mix of resources. Two more wells are now planned in the reservoir to optimize the field potential in both compartments. This will lead to an additional 23% reserves value, giving an overall EUR of ~83% from these compartments.
The main achievement of this study includes the exploitation strategy of a complicated depositional system to optimize a reservoir full of uncertainties. Since the seismic cannot be solely relied upon in such formations; this paper also illustrates the robust engineering models and detailed algorithms to ensure the development of such low-resolution reservoir without incurring major costs. This study offers an innovative approach that can be undertaken to place the wells at optimum locations, along with the utilization of each data set in a more systematic manner to access the, otherwise, undrained locations.
Challenges of Oil and Gas Mega-Projects: Claims Management Process
Authors: Robottom Luke , Tarancon Plata Raquel 
Published: November 2020

Abstract
One of the major challenges of oil and gas mega projects is the claims management process. This paper will discuss how claims, which arise during the lifecycle of a project, can be managed by both the contractor and by the procuring entity. This is particularly relevant to the industry with the recent Covid-19 events and its impacts.
This paper will evaluate and discuss key risks in the implementation of a project – including project cost escalations as well as time overruns and delays to completion of the project or its milestones – and the management of claims in relation thereto. This paper will also discuss how risks may be allocated where a project may be derailed. Secondary impacts will also be considered such as a compromise on quality and potential defects. Lastly, this paper will cover how formal disputes, which arise when claims cannot be adequately resolved, may be brought about and the best position the procuring entity and the contractor can put themselves in for such proceedings.
This paper shall conclude that an optimised "life cycle" approach to procurement is essential. From the beginning of the project, companies should carefully consider how risks are allocated, and from a practical perspective, what the most material risks are that are either likely to occur or could have a significant impact on the project, to ensure in particular that management of those potential risks and impact are adequately addressed in the project documents. During the life of the project there mustbe efficient contract management, it is crucial for the project management team to proactively manage claims in real time to avoid disputes. Lastly, it is key that the company is prepared for formal dispute resolution proceedings by reviewing its position regularly and keeping options open.
This paper draws on our legal expertise based on our vast experience in the field. Based on this experience, this paper applies that knowledge to what is critical in the management of claims throughout the lifecycle of the project. This will assist companies to allocate risk accordingly, mitigate and manage claims and prepare for potential formal disputes.
Smart Way for Consistent Cement Bond Evaluation and Reducing Human Bias Using Machine Learning
Authors: Voleti Deepak Kumar , Reddicharla Nagaraju , Guntupalli Suryanarayana , Reddy Rathnakar , Vanam Prabhaker Reddy , Khanji Maitha Shehab , Sumaidaa Saleh A. , Sultan Ali Mayada Ali 
Published: November 2020

Abstract
Cement evaluation data acquired in oil and gas wells for confirmation of zonal isolation, channeling in cement behind casing and well integrity. All available technologies for cement evaluations are primarily measurements of acoustic parameters like amplitude of first arrival, full waveform recording of refracted wave, impedance and attenuation or there a combination. Generally, operationsPetrophysicist, petroleum engineer or service providers are responsible for evaluation of cement bond logs and propose remedial jobs if required. This cement bond log interpretation is quite subjective if performed through pure visual interpretation and its accuracy depends on objective of well, work pressure and experience of interpreter. This paper talks about how ADNOC Onshore has leveraged machine learning (ML) for interpretation of various cement bond logs from several service providers.
In green fields, cement evaluation is very important in all new wells to ensure good cement quality and zonal isolation and it is also equally important in in brown fields where 1000s of wells were drilled where well integrity issues are of common occurrence. Major challenges in evaluation are inconsistency and human bias in interpretation and as a result, interpretation may vary from one interpreter to the other.
Authors have tested different ML techniques (Random Forest Classification & Neural Net) and smart way of data training. Final recommendation is to use nested models instead of single model. In this technique, input measurements and required solutions will be classified and divided into different classes, a separate ML model will be built for each class and combine all the models to get final cement evaluation and recommendations.
Final results include data quality flags, cement bond quality, zonal isolation, channeling, micro annuals, areal cement map (if analyzed field wise), anomaly maps (if analyzed data from offset wells at different times), and recommendation.
In this project, a simple easy to use user interface has been developed to browse the cement logs and use the trained ML models to predict the cement evaluations with a click of a button.
This ML based cement bond evaluation is proved to be very effective and saved 75% of efforts by operational Petrophysicists. The interpretation accuracy has been significantly improved. This method has potential to be used at rig site. These models are very cost effective with, minimum human bias, and improves consistency as well as independent of wells or type of reservoirs. These models are tested across ADNOC Onshore Wells and can be extended to any well irrespective of geographic locations.
This paper discusses machine-learning approach, evaluation of various algorithms and testing results for cement evaluation log from various service providers.
An Experimental Study to Demonstrate that Nanoparticles can Filter into Shale Formations and Improve Wellbore Stability
Authors: Zhou Nancy , Wu Yongkang , Lu Meng , Li Yucheng , Liu Fuchen , Zhang Guoping 
Published: November 2020

Abstract
For fields with hard and brittle shale sections that have microfractures (either preexisting in the formation or induced by drilling operations), we experienced troublesome drilling with a relatively low rate of penetration (ROP). A systematic experimental work has been implemented to discover whether nanoparticles added to drilling fluids penetrate shale formations and improve wellbore stability.
Starting with quantitative analysis of the shale rocks, we found the mineralogy and clay composition of these rocks by using X-ray diffraction (XRD). The XRD analysis shows that the shale rocks consist of mixed-layer clays and are very heterogeneous. The shale samples were then characterized by X-ray computed tomography (XCT) and scanning electron microscopy (SEM) to gain an understanding of preexisting microfractures. The XCT results show that multiple cracks are irregularly distributed inside the shale with widths between 10 to 25 μm. Smaller cracks with widths ranging from 100 to 250 nm were also observed via imaging on the shale sample surface using SEM. To answer whether adding the nanoparticles improves wellbore stability, we first designed the nanoparticle infiltration test by flowing nanoparticle dispersions through the shale assembly at an inflow pressure of around 800 kPa. After the shale samples were subjected to infiltration tests with durations between 9 to 27 days, the injected depths of nanoparticle along cracks was determined to range from 190 to 420 μm, as seen by optical microscope images. Higher resolution SEM images show that the maximum infiltration depths of the nanoparticles range from 2 to 5 μm in different crack-free locations. Nanoindentation tests were then conducted under the continuous stiffness measurement (CSM) mode to probe the mechanical properties of the pre-treated and post-treated shale samples. At two indentation depths, the averaged Young's modulus E for the shale sample before and after nanoparticle infiltration significantly increased from 29.5 to 34.0 GPa (at a depth of 500 nm) and from 29.0 to 33.3 GPa (at a depth 3000 nm), respectively. The nanoparticle infiltration and nanoindentation test results demonstrate that nanoparticles can filter into and further strengthen the shale formation.
We also formulated nanoparticles in our drilling fluid and performed the pressure transmission test (PTT) to see their sealing effect on the field cores. A highly favorable plugging result was obtained from the PTT using base mud with nanoparticles. Therefore, it is concluded that our systematic experimental work has delivered results, which support the practice of using nanoparticles to improve wellbore stability for shale formations.
Subsurface Velocity Profiling by Application of Physics Informed Neural Networks
Authors: Kumar Ashutosh , Murali Amal , Priyadarshan Amit 
Published: November 2020

Abstract
Subsurface imaging techniques with improved resolution is the industry's pressing challenge. In this paper, we present a novel Machine Learning based data processing method to generate accurate seismic profiles. Through this method, the processing of humongous acoustic data and subsequent inference of subsurface properties is significantly expedited. The work being presented is an application of a recently developed novel class of algorithms called the Physics Informed Neural Networks (PINNs).
The method has been proven in its efficacy for approximating the solutions to Partial Differential Equations (PDE) governing complex physical phenomena and in data-driven discovery of the underlying PDEs such as the Navier-Stokes system.
Herein, we have followed a two-stage procedure. Stage one is the accurate prediction of spatiotemporal propagation of a seismic pulse through a simulated domain resembling thesubsurface. For this we developed a simulator to generate vast amounts of virtual seismic data. Dense Deep Neural Networks (DNN) were used to overfit the data so that it approximates the solution to the wave equations. This was followed by the data-driven discovery of local velocities in different reflector layers. In the second stage, we have extended the algorithm to infer the real subsurface velocity profile based on the data collected at receiver positions during an actual seismic survey from Volve, a field on the Norwegian continental shelf.
Moreover, we have bench-marked and established the performance of various models in their ability to learn the receiver data obtained from a simulated 2D seismic survey. The results are promising as the network chosen can learn the receiver data with a minimal number of iterations and with near-perfect accuracy. The models were further tested in their ability to represent solutions of the wave equation in the spatiotemporal domain and in their ability to represent the actual subsurface velocity profiles.
The novelty of this paper lies in the overlap of a nascent-stage deep learning tool, called PINNs, with seismic data processing to expedite and improvise high-resolution subsurface imaging. For this, various other uncommon developments were done such as that of adapting PINN as a dynamic wave function approximator. Moreover, the effectiveness of the proposed method is numerically demonstrated by the excellent accuracy obtained when validated against the velocity profile from the field as well as the velocity profile from the simulator.
Progressive Collaborative Planning Approach
Authors: Elkhozahi Hany , Hamdan Hanan , Alhadhrami Abdulla 
Published: November 2020

Abstract
Upper Zakum field is a mega offshore field, in which ADNOC Offshore is investing a lot of resources to bring development target to reality. In order to achieve the company's strategic milestones, over 300 minor and major projects are being implemented utilizing a tremendous number of major and minor resources. This excessive volume of data is communicated daily from each division independently with different templates/format, which isn't reflecting the complete image of operations and development progress status. Therefore, to achieve those strategic milestones, an effective planning process across multiple functions within different organizations and at various locations, along with an efficient tracking system was established. Operation Planning & Support teams developed Upper Zakum 5 years business plan alongside the Integrated Master Activity Plan (IMAP) to identify the required scope ensuring safe guidance of the company towards achieving its targets while sustaining the integrity. IMAP leverages the communication level among all stakeholders, particularly during the planning phase, to enhance the effectiveness and realism of plans, in addition to, providing flexibility in reviewing the plan from different perspectives through the ability to produce various layouts aligned with specific requirements. IMAP website has been established to enhance communication and ensure that all stakeholders have adequate information that supports maintaining consistency in the workplace and preventing any ambiguity. The website provides an efficient and prompt access to review planned activities for a particular project as well as forecasted activities on a particular asset location. IMAP website has ensured the effective distribution of up to date information on each asset location such as objectives, responsible team, resources allocation, material delivery time, execution timeline, and planned shutdown windows. This collaborative approach leads to early discovering of major resources clashes and abolishes them during the planning phase, ensuring the best utilization of resources and completing the required scope within the time boundary; as well as, enhancing the predictions of production, water injection and improve the accuracy of budget outlook; in addition to data-driven realistic decision-making.
Enhancing Foam Stability Through a Combination of Surfactant and Nanoparticles
Authors: Almubarak Majed , AlYousef Zuhair , Almajid Muhammad , Almubarak Tariq , Ng Jun Hong 
Published: November 2020

Abstract
Gas injection is amongst the oldest and most used enhanced oil recovery (EOR) methods in the petroleum industry. Nevertheless, gas EOR is subject to poor macroscopic sweep efficiency due to the higher mobility and lower density of gas compared to reservoir fluids. Foamed-gas injection can regulate the mobility of gas by trapping a large fraction of the gas inside the porous medium, thereby increasing its apparent viscosity and reducing its relative permeability. However, the poor stability of foam at harsh reservoir temperature and salinity conditions is a major limitation that hinders the effectiveness of the foam flood. A combination of surfactant and nanoparticles (NPs) provides a novel solution to foam stability challenges.
This study evaluates the role of NPs on enhancing foam stability. Static and dynamic laboratory tests were conducted along with particle size and zeta potential measurements to capture the foam stability and strength in porous media for a cationic surfactant combined with surface modified silica NPs. The static bulk foam stability was determined by measuring the foam half-life over time. The dynamic foam stability was determined through the mobility reduction factor (MRF) using a micromodel apparatus. The tests were carried out on a variety of NPs concentrations and fixed surfactant concentration.
The results from the experiments show that the use of surfactant combined with NPs enhances the stability and strength of the generated foam when compared to the use of surfactant alone. The foam static tests show that the mixture of NPs and surfactants produces foams with smaller bubbles and longer half-life when compared to those in the absence of NPs. The results also demonstrate that the concentration of surfactant and NPs is a crucial parameter for foam stability and that there is an optimal concentration of NPs for strong foam generation. In porous media, the addition of NPs to surfactant solutions results in larger pressure differences across the micromodel chip and, accordingly, greater reduction of gas mobility when compared to those using surfactant solution alone. The results also reveal that the generation of NPs flocs is the main mechanism of foam stabilization enhancement.
This work shows that using NPs at carefully selected concentrations in combination with surfactant can improve the foam static and dynamic stability in porous media, effectively lowering the mobility of the injected fluids and, eventually, improving the sweep efficiency of gas compared to the typical application of using surfactant alone.