Modeling the Pressure Transverse for Foam Drilling Operation in Vertical Well

2021 ◽  
Author(s):  
Adesina Fadairo ◽  
Gbadegesin Adeyemi ◽  
Kegang Ling ◽  
Vamegh Rasouli ◽  
Adedayo Iroko ◽  
...  
Keyword(s):  
Drill Pipe ◽  
Flow Region ◽  
Percentage Error ◽  
Drilling Operation ◽  
Underbalanced Drilling ◽  
Proposed Model ◽  
Hydraulic Design ◽  
Foam Drilling ◽  
Function Time

Abstract Pressure transverse in foam drilling operation is sensitive and difficult to predict particular at the start of flow that follows the unavoidable shut in due to inevitable procedure of stop and proceed arising from re-connection of additional drilling pipe to further drill depth. The practice in drilling may not enable the flow to attain steadiness flow region before running in the length of drill pipe. Most existing models in the literature for predicting pressure transverse in foam drilling operation only captured the steadiness flow region of the foam drilling operation by keeping out restriction terms induced by accumulation and kinetic for simplicity sake, hence unsteadiness flow region experienced during foam drilling operation was rarely modelled. It is highly expedient to derive a model that evident the unsteadiness region in order to accurately predict pressure transverse, hence sufficiently analyses the well stability during foam drilling operation. In this study, a model for forecasting pressure transverse in foam drilling operation was established considering restriction term caused by accumulation and kinetic that constitute for accurate formulation of hydraulic model that govern flow of foam during underbalanced drilling. By applying the proposed model to a case study reported in literature, pressure transverse at unsteadiness flow region for foam drilling operation can be quantitatively estimated and analyzed. The result obtained in a case study carried out indicates high variance in pressure as function time at the beginning of flow in foam drilling where unsteadiness is promoted before matching up closely with the results obtained from the existing Guo et al 2003 model at the steadiness flow region. The new model has a better accuracy with a percentage error of 0.74% and 6.4% as compared to previous models by Guo et al 2003. The proposed model make possible for drilling engineer to take decision with larger precision during hydraulic design of foam drilling operation and guaranteeing well stability in complex drilling system.

scholarly journals Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1068
Author(s):  
Jiajia Xu ◽  
Li Zhou ◽  
Ge He ◽  
Xu Ji ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Pearson Correlation ◽  
Influential Factors ◽  
Structural Safety ◽  
Percentage Error ◽  
Modeling Framework ◽  
Machine Material ◽  
Proposed Model

Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

scholarly journals Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia

2020 ◽  
Vol 173 ◽  
pp. 01004
Author(s):  
Yunus Parvej Faniband ◽  
S. M. Shaahid
Keyword(s):  
Neural Networks ◽  
Saudi Arabia ◽  
Renewable Energy ◽  
Wind Speed ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Support Vector ◽  
Wind Speed Data ◽  
Proposed Model

The growing concerns regarding the depletion of oil/gas reserves and global warming have made it inevitable to seek energy from wind and other renewable energy resources. Forecasting wind speed is a challenging topic and has important applications in the design and operation of wind power systems, particularly grid connected renewable energy systems, and where forecasting wind speed helps in manipulating the load on the grid. Modern machine learning techniques including neural networks have been widely used for this purpose. As per literature, various models for estimating the hourly wind speed one hour ahead and the hourly wind speed data profile one day ahead have been developed. This paper proposes the use of Artificial Intelligence methods (AI) which are most suitable for the prediction and have provided best results in many situations. AI method involves nonlinear (or linear) and highly complex statistical relationships between input and output data, such as neural networks, fuzzy logic methods, Knearest Neighbors algorithm (KNN) and Support Vector Machine (SVM). AI methods are promising alternatives for predicting wind speed and understanding the wind behavior for a particular region. In the present study (as a case-study), hourly average wind speed data of 13 years (1970-1982) of Qaisumah, Saudi Arabia has been used to evaluate the performance of ANN model. This data has been used for training the neural network. ANN is trained multiple times with different number of hidden neurons to forecast accurate wind speed. The efficiency of proposed model is validated by predicting wind speed of the Qaisumah region with the measured data. Mean Square Error (MSE) and mean absolute percentage error (MAPE values) for proposed model are found to be 0.0912 and 6.65% respectively.

Underbalanced MPD Technology Proved to be Effective in Drilling of Fractured Granitic Basement

2016 ◽  
Author(s):  
Peng Chen ◽  
Xi Wang ◽  
Meng Wu
Keyword(s):  
Potential Problem ◽  
Drilling Fluid ◽  
Pressure Coefficient ◽  
Pressure Profile ◽  
Underbalanced Drilling ◽  
Managed Pressure Drilling ◽  
Basement Reservoirs ◽  
Almost All ◽  
Foam Drilling

Abstract The basement hydrocarbon reservoirs have been discovered in a lot of places over the world. The remarkable characteristics of the basement reservoirs are their low pressure with narrow density windows and well developed fractures which usually resulted in probably massive losses. A case study on drilling of fractured granitic basement with application of UBD in Chad and MPD in Indonesia is presented in this paper. To tackle the common problems of drilling in narrow density windows and potential problem of losses, an underbalanced drilling (UBD) technology with a micro-foam drilling fluid was used in Chad. The pore pressure coefficient of the basement of Chad was predicted as between 1.02-1.06, and the density of the micro-foam drilling fluid was designed to be 8.7ppg. While an under-balanced managed pressure drilling (MPD) technology with a synthetic based gas-to-liquid (GTL) drilling fluid was utilized in Indonesia. The formation pressure coefficient of the basement of Indonesia was estimated to be 1.04, and the density of the GTL drilling fluid was designed to be 7.4ppg. Losses or severe losses existed in previous conventional near-balanced drilling in fractured granitic basement of buried hills of Chad. The problem of losses also encountered even UBD was later used. Losses and kicks continued almost all the time during drilling, coring and wireline logging in some wells. Losses happened as soon as pump started while overflow occurred no sooner than pump stopped. However, the potential problem of losses and kicks was completely controlled by utilization of under-balanced MPD technology in fractured granitic basement of Indonesia. The under-balanced MPD technology, a precisely pressure controlled drilling system, is able to accurately control the annular pressure profile throughout the wellbore, therefore it could effectively achieve safe drilling in narrow density window and cut non-production time. It is proved to be effective in drilling of fractured granitic basement.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

scholarly journals Toward Decentralised Sanitary Sewage Collection Systems: A Multiobjective Approach for Cost-Effective and Resilient Designs

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1886
Author(s):  
Arezoo Zahediasl ◽  
Amin E. Bakhshipour ◽  
Ulrich Dittmer ◽  
Ali Haghighi
Keyword(s):  
Treatment Plant ◽  
Drainage System ◽  
Cost Effective ◽  
Optimization Approach ◽  
Hydraulic Design ◽  
Multiobjective Approach ◽  
Layout Generation ◽  
Plant Allocation ◽  
Problem Solving Process

In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.

scholarly journals A New Approach to Inform Restoration and Management Decisions for Sustainable Apiculture

2021 ◽  
Vol 13 (11) ◽  
pp. 6109
Author(s):  
Joanne Lee Picknoll ◽  
Pieter Poot ◽  
Michael Renton
Keyword(s):  
Floral Resources ◽  
Pollination Services ◽  
New Approach ◽  
Honey Production ◽  
Proposed Model ◽  
Colony Loss ◽  
Supply Potential ◽  
Australian Case ◽  
Stocking Rates

Habitat loss has reduced the available resources for apiarists and is a key driver of poor colony health, colony loss, and reduced honey yields. The biggest challenge for apiarists in the future will be meeting increasing demands for pollination services, honey, and other bee products with limited resources. Targeted landscape restoration focusing on high-value or high-yielding forage could ensure adequate floral resources are available to sustain the growing industry. Tools are currently needed to evaluate the likely productivity of potential sites for restoration and inform decisions about plant selections and arrangements and hive stocking rates, movements, and placements. We propose a new approach for designing sites for apiculture, centred on a model of honey production that predicts how changes to plant and hive decisions affect the resource supply, potential for bees to collect resources, consumption of resources by the colonies, and subsequently, amount of honey that may be produced. The proposed model is discussed with reference to existing models, and data input requirements are discussed with reference to an Australian case study area. We conclude that no existing model exactly meets the requirements of our proposed approach, but components of several existing models could be combined to achieve these needs.

Elastic Averaging in Flexure Mechanisms: A Muliti-Beam Parallelogram Flexure Case-Study

2006 ◽  
Author(s):  
Shorya Awtar ◽  
Edip Sevincer
Keyword(s):  
Mechanism Design ◽  
Parametric Model ◽  
Nonlinear Load ◽  
Geometric Imperfections ◽  
Geometric Imperfection ◽  
Flexure Mechanism ◽  
Important Concern ◽  
Proposed Model ◽  
Geometric Arrangements

Over-constraint is an important concern in mechanism design because it can lead to a loss in desired mobility. In distributed-compliance flexure mechanisms, this problem is alleviated due to the phenomenon of elastic averaging, thus enabling performance-enhancing geometric arrangements that are otherwise unrealizable. The principle of elastic averaging is illustrated in this paper by means of a multi-beam parallelogram flexure mechanism. In a lumped-compliance configuration, this mechanism is prone to over-constraint in the presence of nominal manufacturing and assembly errors. However, with an increasing degree of distributed-compliance, the mechanism is shown to become more tolerant to such geometric imperfections. The nonlinear load-stiffening and elasto-kinematic effects in the constituent beams have an important role to play in the over-constraint and elastic averaging characteristics of this mechanism. Therefore, a parametric model that incorporates these nonlinearities is utilized in predicting the influence of a representative geometric imperfection on the primary motion stiffness of the mechanism. The proposed model utilizes a beam generalization so that varying degrees of distributed compliance are captured using a single geometric parameter.

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