Estimation of Cuttings Concentration and Frictional Pressure Losses During Drilling Using Data-Driven Models

2021 ◽  
Author(s):  
Murat Ozbayoglu ◽  
Evren Ozbayoglu ◽  
Baris Guney Ozdilli ◽  
Oney Erge
Keyword(s):  
Oil And Gas ◽  
Mechanistic Model ◽  
Data Driven ◽  
P Value ◽  
Cuttings Transport ◽  
Frictional Loss ◽  
Pressure Losses ◽  
Dimensionless Groups ◽  
Wide Range ◽  
Pipe Rotation

Abstract Drilling practice has been evolving parallel to the developments in the oil and gas industry. Current supply and demand for oil and gas dictate search for hydrocarbons either at much deeper and hard-to-reach fields, or at unconventional fields, both requiring extended reach wells, long horizontal sections, and 3D complex trajectories. Cuttings transport is one of the most challenging problems while drilling such wells, especially at mid-range inclinations. For many years, numerous studies have been conducted to address modeling of cuttings transport, estimation of the concentration of cuttings as well as pressure losses inside the wellbores, considering various drilling variables having influence on the process. However, such attempts, either mechanistic or empirical, have many limitations due to various simplifications and assumptions made during the development stage. Fluid thixotropy, temperature variations in the wellbore, uncertainty in pipe eccentricity as well as chaotic motion of cuttings due to pipe rotation, imperfections in the wellbore walls, variations in the size and shape of the cuttings, presence of tool joints on the drillstring, etc. causes the modeling of the problem extremely difficult. Due to the complexity of the process, the estimations are usually not very accurate, or not reliable. In this study, data-driven models are used to address the estimation of cuttings concentration and frictional loss estimation in a well during drilling operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa – Drilling Research Projects, which includes a wide range of wellbore and pipe sizes, inclinations, ROPs, pipe rotation speeds, flow rates, fluid and cuttings properties. The evaluation of the models is conducted using Root Mean Square Error, R-Squared Values, and P-Value. As the inputs of the data-driven models, independent drilling variables are directly used. Also, as a second approach, dimensionless groups are developed based on these independent drilling variables, and these dimensionless groups are used as the inputs of the models. Moreover, performance of the data-driven model results are compared with the results of a conventional mechanistic model. It is observed that in many cases, data-driven models perform significantly better than the mechanistic model, which provides a very promising direction to consider for real time drilling optimization and automation. It is also concluded that using the independent drilling variables directly as the model inputs provided more accurate results when compared with dimensional groups are used as the model inputs.

Simple Correlations and Analysis of Cuttings Transport With Newtonian and Non-Newtonian Fluids in Horizontal and Deviated Wells

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Mehmet Sorgun
Keyword(s):  
Pure Water ◽  
Newtonian Fluids ◽  
The Other ◽  
Cuttings Transport ◽  
Pressure Losses ◽  
Test Conditions ◽  
Bed Thickness ◽  
Solid Liquid ◽  
Cuttings Bed ◽  
Pipe Rotation

In this study, simple empirical frictional pressure losses and cuttings bed thickness correlations including pipe rotation are developed for solid-liquid flow in horizontal and deviated wellbores. Pipe rotation effects on cuttings transport in horizontal and highly inclined wells are investigated experimentally. Correlations are validated experimental data with pure water as well as four different non-Newtonian fluids for hole inclinations from horizontal to 60 degrees, flow velocities from 0.64 m/s to 3.56 m/s, rate of penetrations from 0.00127 to 0.0038 m/s, and pipe rotations from 0 to 250 rpm. Pressure drop within the test section, and stationary and/or moving bed thickness are recorded besides the other test conditions. The new correlations generated in this study are believed to be very practical and handy when they are used in the field.

Statistical Significance and Hypothesis for Friction Factor Correlations of Non-Newtonian Crude Oils in Pipelines

2015 ◽  
Author(s):  
Ahmed H. Kamel ◽  
Ali S. Shaqlaih ◽  
Essam A. Ibrahim
Keyword(s):  
Turbulent Flow ◽  
Friction Factor ◽  
Oil And Gas ◽  
Flow Behavior ◽  
Oil And Gas Industry ◽  
Newtonian Fluids ◽  
Data Set ◽  
Gas Industry ◽  
Pressure Losses ◽  
Wide Range

In pipelines, non-Newtonian fluids are generally pumped under turbulent flow conditions where frictional pressure losses are required for hydraulic design. The friction factor is a crucial parameter in calculating frictional pressure losses. However, determination of the friction factor is a decisive challenge, especially for turbulent flow of non-Newtonian fluids. This is mainly due to the large number of friction factor equations and the precision of each. The main objective of the present paper is to evaluate the published friction factor correlations for non-Newtonian fluids over a wide range of friction factor data to select the most accurate one. An analytical comparative study adopting the recently introduced Akaike information criterion (AIC) and the traditional coefficient of determination (R2) is conducted. Data reported by several researchers are used individually and collectively. The results show that each model exhibits accuracy when examined with a specific data set while El-Emam et al. model proves its superiority to other models when examining the data mutually. In addition to its simple and explicit form, it covers a wide range of flow behavior indices and generalized Reynolds numbers. It is also shown that the traditional belief that a higher R2 corresponds to better models may be misleading. AIC overcomes the shortcomings of R2 as it employs the parsimonious principle to trade between the complexity of the model and its accuracy not only to find the best approximating model but also to develop statistical inference based on the data. Although it has not yet been used in oil and gas industry, the authors present the AIC to initiate an innovative strategy that has been demonstrated in other disciplines to help alleviate several challenges faced by professionals in the oil and gas industry. Finally, a detailed discussion and models’ ranking according to AIC and R2 is presented showing the numerous advantages of AIC.

scholarly journals Optimization of Flow Rate and Pipe Rotation Speed Considering Effective Cuttings Transport Using Data-Driven Models

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1484
Author(s):  
Evren Ozbayoglu ◽  
Murat Ozbayoglu ◽  
Baris Guney Ozdilli ◽  
Oney Erge
Keyword(s):  
Flow Rate ◽  
Rotation Speed ◽  
Design Tool ◽  
Data Driven ◽  
Mechanistic Models ◽  
Operational Parameters ◽  
Lost Circulation ◽  
Wide Range ◽  
Using Data ◽  
Pipe Rotation

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa–Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rates, and fluid and cuttings properties. It is observed that, in many cases, the data-driven model significantly outperforms the mechanistic models, which provides a very promising direction for real-time drilling optimization and automation. After the neural network is proven to work effectively, an optimization attempt to estimate flow rate and pipe rotation speed is introduced using a genetic algorithm. The decision is made considering minimizing the required total energy for this process. This approach may be used as a design tool to identify the required flow rate and pipe rotation speed to acquire effective hole cleaning while consuming minimal energy.

scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

scholarly journals Natural Experiment on Trade and Investment Liberalization and Soft Drink Consumption

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
Y Tcholakov
Keyword(s):  
Public Health ◽  
International Trade ◽  
Natural Experiment ◽  
Soft Drink ◽  
Trade Agreement ◽  
P Value ◽  
Soft Drink Consumption ◽  
Obesity And Diabetes ◽  
Wide Range ◽  
The Impact

Abstract Background Globalization is recognized to as a contributing factor to a health harming environment through a variety of mechanisms including through changes in food systems and food availability. Sugar-sweetened beverage (SSB) consumption is linked to obesity and diabetes and its regulation is a key priority for public health. The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is an international trade agreement between 11 countries. Methods This project uses of natural experiment methods to predict the impact of the entry into force of the CPTPP on SSB consumption. These methods allow quantitative inferences to be drawn in the situations where the exposure is not randomly assigned. Soft drink consumption data was collected from the Euromonitor database for 80 countries from all regions. This data was used to estimate the effect of agreements similar to the TPP. Results Eleven country trade agreement pairs were identified. In 5 cases out of the 11, the exposed country had a higher soft drink consumption at five years after the trade agreement. The effect of the trade agreement exposure for an average country in the sample in a trade agreement was found to be 1.10 (95% CI: 1.01-1.18; p-value: 0.03) after adjusting for GDP and the involvement of the US. In 7 of the 11 member-countries soft drink consumption is expected to increase yielding an average increase of 9.0% in those countries; the changes did not yield statistically significant differences in others. Conclusions This projected extended the use of synthetic methods to the projection of future effects of policy implementation. While it showed that there may be increasing trend of SSB consumption in certain scenarios, this could not be generalized to all cases. This illustrates the wide range of effects of international trade liberalization and highlights that national policy probably plays a strong modulating role on the impact that it has on local food environments. Key messages Globalization can lead to health harming environments and its impacts should further be studied by public health professionals and researchers. Many global policies have the potential to lead to significant health impacts but are negotiated without involving public health experts.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Study the effect of casting temperature on details from thermo-plastic materials

2020 ◽  
pp. 42-45
Author(s):  
J.A. Kerimov ◽  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Mold Temperature ◽  
Quality Parameters ◽  
Oil Field ◽  
Casting Temperature ◽  
Gas Industry ◽  
Field Equipment ◽  
Wide Range ◽  
The Impact

The implementation of plastic details in various constructions enables to reduce the prime cost and labor intensity of machine and device manufacturing, decrease the weight of design and improve their quality and reliability at the same time. The studies were carried out with the aim of labor productivity increase and substitution of colored and black metals with plastic masses. For this purpose, the details with certain characteristics were selected for further implementation of developed technological process in oil-gas industry. The paper investigates the impact of cylinder and compression mold temperature on the quality parameters (shrinkage and hardness) of plastic details in oil-field equipment. The accessible boundaries of quality indicators of the details operated in the equipment of exploration, drilling and exploitation of oil and gas industry are studied in a wide range of mode parameters. The mathematic dependences between quality parameters (shrinkage and hardness) of the details on casting temperature are specified.

Nanoemulsions: A Versatile Technology for Oil and Gas Applications

2021 ◽  
Author(s):  
Nouf AlJabri ◽  
Nan Shi
Keyword(s):  
Droplet Size ◽  
Large Scale ◽  
Oil And Gas ◽  
Volume Fraction ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
High Energy ◽  
Small Droplet ◽  
Gas Industry ◽  
Wide Range

Abstract Nanoemulsions (NEs) are kinetically stable emulsions with droplet size on the order of 100 nm. Many unique properties of NEs, such as stability and rheology, have attracted considerable attention in the oil industry. Here, we review applications and studies of NEs for major upstream operations, highlighting useful properties of NEs, synthesis to render these properties, and techniques to characterize them. We identify specific challenges associated with large-scale applications of NEs and directions for future studies. We first summarize useful and unique properties of NEs, mostly arising from the small droplet size. Then, we compare different methods to prepare NEs based on the magnitude of input energy, i.e., low-energy and high-energy methods. In addition, we review techniques to characterize properties of NEs, such as droplet size, volume fraction of the dispersed phase, and viscosity. Furthermore, we discuss specific applications of NEs in four areas of upstream operations, i.e., enhanced oil recovery, drilling/completion, flow assurance, and stimulation. Finally, we identify challenges to economically tailor NEs with desired properties for large-scale upstream applications and propose possible solutions to some of these challenges. NEs are kinetically stable due to their small droplet size (submicron to 100 nm). Within this size range, the rate of major destabilizing mechanisms, such as coalescence, flocculation, and Ostwald ripening, is considerably slowed down. In addition, small droplet size yields large surface-to-volume ratio, optical transparency, high diffusivity, and controllable rheology. Similar to applications in other fields (food industry, pharmaceuticals, cosmetics, etc.), the oil and gas industry can also benefit from these useful properties of NEs. Proposed functions of NEs include delivering chemicals, conditioning wellbore/reservoir conditions, and improve chemical compatibility. Therefore, we envision NEs as a versatile technology that can be applied in a variety of upstream operations. Upstream operations often target a wide range of physical and chemical conditions and are operated at different time scales. More importantly, these operations typically consume a large amount of materials. These facts not only suggest efforts to rationally engineer properties of NEs in upstream applications, but also manifest the importance to economically optimize such efforts for large-scale operations. We summarize studies and applications of NEs in upstream operations in the oil and gas industry. We review useful properties of NEs that benefit upstream applications as well as techniques to synthesize and characterize NEs. More importantly, we identify challenges and opportunities in engineering NEs for large-scale operations in different upstream applications. This work not only focuses on scientific aspects of synthesizing NEs with desired properties but also emphasizes engineering and economic consideration that is important in the oil industry.

Energy Harvesting Under Harsh Conditions for the Oil & Gas Upstream Industry

2021 ◽  
Author(s):  
José Correia ◽  
Cátia Rodrigues ◽  
Ricardo Esteves ◽  
Ricardo Cesar Bezerra de Melo ◽  
José Gutiérrez ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Oil And Gas ◽  
Electrical Energy ◽  
Development Stage ◽  
Gas Extraction ◽  
Power Monitoring ◽  
Operational Conditions ◽  
Wide Range ◽  
First Time ◽  
Harsh Conditions

Abstract Environmental and safety sensing is becoming of high importance in the oil and gas upstream industry. However, present solutions to feed theses sensors are expensive and dangerous and there is so far no technology able to generate electrical energy in the operational conditions of oil and gas extraction wells. In this paper it is presented, for the first time in a relevant environment, a pioneering energy harvesting technology based on nanomaterials that takes advantage of fluid movement in oil extraction wells. A device was tested to power monitoring systems with locally harvested energy in harsh conditions environment (pressures up to 50 bar and temperatures of 50ºC). Even though this technology is in an early development stage this work opens a wide range of possible applications in deep underwater environments and in Oil and Gas extraction wells where continuous flow conditions are present.

P–530 The use of wide thresholds for detecting intermediate chromosomal CNV up to 80% doesn’t improve PGT-A ability to discriminate true mosaic from uniformly aneuploid embryos

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
L Girardi ◽  
M Serdaroğulları ◽  
C Patassini ◽  
S Caroselli ◽  
M Costa ◽  
...  
Keyword(s):  
Inner Cell Mass ◽  
False Negative ◽  
Cell Mass ◽  
Categorical Variables ◽  
P Value ◽  
Sequencing Data ◽  
Preimplantation Genetic Testing ◽  
Exact Test ◽  
Institutional Review ◽  
Wide Range

Abstract Study question What is the effect of varying diagnostic thresholds on the accuracy of Next Generation Sequencing (NGS)-based preimplantation genetic testing for aneuploidies (PGT-A)? Summary answer When single trophectoderm biopsies are tested, the employment of 80% upper threshold increases mosaic calls and false negative aneuploidy results compared to more stringent thresholds. What is known already Trophectoderm (TE) biopsy coupled with NGS-based PGT-A technologies are able to accurately predict Inner Cell Mass’ (ICM) constitution when uniform whole chromosome aneuploidies are considered. However, minor technical and biological inconsistencies in NGS procedures and biopsy specimens can result in subtle variability in analytical results. In this context, the stringency of thresholds employed for diagnostic calls can lead to incorrect classification of uniformly aneuploid embryos into the mosaic category, ultimately affecting PGT-A accuracy. In this study, we evaluated the diagnostic predictivity of different aneuploidy classification criteria by employing blinded analysis of chromosome copy number values (CNV) in multifocal blastocyst biopsies. Study design, size, duration The accuracy of different aneuploidy diagnostic cut-offs was assessed comparing chromosomal CNV in intra-blastocysts multifocal biopsies. Enrolled embryos were donated for research between June and September 2020. The Institutional Review Board at the Near East University approved the study (project: YDU/20l9/70–849). Embryos diagnosed with uniform chromosomal alterations (single or multiple) in their clinical TE biopsy (n = 27) were disaggregated into 5 portions: the ICM and 4 TE biopsies. Overall, 135 specimens were collected and analysed. Participants/materials, setting, methods Twenty-seven donated blastocysts were warmed and disaggregated in TE biopsies and ICM (n = 135 biopsies). PGT-A analysis was performed using Ion ReproSeq PGS kit and Ion S5 sequencer (ThermoFisher). Sequencing data were blindly analysed with Ion-Reporter software. Intra-blastocyst comparison of raw NGS data was performed employing different thresholds commonly used for aneuploidy classification. CNV for each chromosome were reported as aneuploid according to 70% or 80% thresholds. Categorical variables were compared using Fisher’s exact test. Main results and the role of chance In this study, a total of 50 aneuploid patterns in 27 disaggregated embryos were explored. Single TE biopsy results were considered as true positive when they displayed the same alteration detected in the ICM at levels above the 70% or 80% thresholds. Alternatively, alterations detected in the euploid or mosaic range were considered as false negative aneuploidy results. When the 70% threshold was applied, aneuploidy findings were confirmed in 94.5% of TE biopsies analyzed (n = 189/200; 95%CI=90.37–37.22), while 5.5% showed a mosaic profile (50–70%) but uniformly abnormal ICM. Positive (PPV) and negative predictive value (NPV) per chromosome were 100.0% (n = 189/189; 95%CI=98.07–100.00) and 99.5% (n = 2192/2203; 95%CI=99.11–99.75) respectively. When the upper cut-off was experimentally placed at 80% of abnormal cells, a significant decrease (p-value=0.0097) in the percentage of confirmed aneuploid calls was observed (86.5%; n = 173/200; 95%CI=80.97–90.91), resulting in mosaicism overcalling, especially in the high range (50–80%). Less stringent thresholds led to extremely high PPV (100.0%; n = 173/173; 95%CI=97.89–100.00), while NPV decreased to 98.8% (n = 2192/2219; 95%CI=98.30–99.23). Furthermore, no additional true mosaic patterns were identified with the use of wide range thresholds for aneuploidy classification. Limitations, reasons for caution This approach involved the analysis of aneuploidy CNV thresholds at the embryo level and lacked from genotyping-based confirmation analysis. Moreover, aneuploid embryos with known meiotic partial deletion/duplication were not included. Wider implications of the findings: The use of wide thresholds for detecting intermediate chromosomal CNV up to 80% doesn’t improve PGT-A ability to discriminate true mosaic from uniformly aneuploid embryos, lowering overall diagnostic accuracy. Hence, a proportion of the embryos diagnosed as mosaic using wide calling thresholds may actually be uniformly aneuploid and inadvertently transferred. Trial registration number N/A

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