scholarly journals Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

2017 ◽  
Vol 62 (1) ◽  
pp. 131-144 ◽  
Author(s):  
Sina Norouzi Bezminabadi ◽  
Ahmad Ramezanzadeh ◽  
Seyed-Mohammad Esmaeil Jalali ◽  
Behzad Tokhmechi ◽  
Abbas Roustaei
Keyword(s):  
Correlation Coefficients ◽  
Physical And Mechanical Properties ◽  
Environmental Parameters ◽  
Oil Field ◽  
Rock Properties ◽  
Oil Well ◽  
Ann Model ◽  
Operation Performance ◽  
Southwest Of Iran ◽  
Intelligent Methods

Abstract Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method.

scholarly journals The Influence of Kaolinite - Illite toward mechanical properties of Claystone

2019 ◽  
Vol 11 (1) ◽  
pp. 440-446
Author(s):  
Supandi Supandi ◽  
Zufialdi Zakaria ◽  
Emi Sukiyah ◽  
Adjat Sudradjat
Keyword(s):  
Mechanical Properties ◽  
Physical Properties ◽  
Void Ratio ◽  
Correlation Coefficients ◽  
Physical And Mechanical Properties ◽  
Friction Angle ◽  
Engineering Properties ◽  
Rock Properties ◽  
Triaxial Tests ◽  
Mineralogical Characteristics

Abstract This study investigates the relationship between clay minerals (kaolinite and illite) and rock properties of the claystone, including both mechanical (cohesion, friction angle, stress, and strain) and physical properties (natural water content, void ratio, and wet density), belonging to Warukin Formation of Kalimantan, Indonesia. Mineralogical characteristics of these rocks were studied using petrological and X-ray diffraction techniques, whereas the mechanical and physical properties were tested by conducting uniaxial and triaxial tests. Relationship among the variables was determined using correlation coefficients. It was observed that the mineralogy of the rocks pose strong constraints on their engineering properties. The results showed that an increase in illite content decreases cohesion, friction angle, strength, and safety factor; and increases natural moisture content, void ratio, and wet density. Although illite content of these rocks was just about 10.8% of the total minerals, it has significantly contributed to the modification of physical and mechanical properties. In contrast, kaolinite did not have a significant impact; since the correlation between various parameters was significantly low (correlation coefficient was much less, <0.3). Therefore while selecting the materials for geotechnical engineering applications, illite emerges as a safer alternative to kaolinite, especially when its concentration is less than 10.8% of the total rock mass.

scholarly journals Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques

2021 ◽  
Author(s):  
Osama Siddig ◽  
Salaheldin Elkatatny
Keyword(s):  
Young’S Modulus ◽  
Elastic Moduli ◽  
Young's Modulus ◽  
Correlation Coefficients ◽  
Wellbore Stability ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Weight On Bit ◽  
Data Points

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.

Downhole CO2 partial pressure calculation and tubing material selection – a case study of an offshore oil field in the South China Sea

2016 ◽  
Vol 63 (5) ◽  
pp. 414-420 ◽  
Author(s):  
Wei Yan ◽  
Yong Xiang ◽  
Wenliang Li ◽  
Jingen Deng
Keyword(s):  
Partial Pressure ◽  
Pressure Profile ◽  
Field Application ◽  
Separation Process ◽  
Oil Field ◽  
Oil Well ◽  
Content Type ◽  
Calculating Method ◽  
Oil Gas

Purpose This paper aims to establish the downhole CO2 partial pressure profile calculating method and then to make an economical oil country tubular goods (OCTG) anti-corrosion design. CO2 partial pressure is the most important parameter to the oil and gas corrosion research for these wells which contain sweet gas of CO2. However, till now, there has not been a recognized method for calculating this important value. Especially in oil well, CO2 partial pressure calculation seems more complicated. Based on Dolton partial pressure law and oil gas separation process, CO2 partial pressure profile calculating method in oil well is proposed. A case study was presented according to the new method, and two kinds of corrosion environment were determined. An experimental research was conducted on N80, 3Cr-L80 and 13Cr-L80 material. Based on the test results, 3Cr-L80 was recommended for downhole tubing. Combined with the field application practice, 3Cr-L80 was proved as a safety and economy anti-corrosion tubing material in this oil field. A proper corrosion parameter (mainly refers to CO2 partial pressure and temperature) can ensure a safety and economy downhole tubing anti-corrosion design. Design/methodology/approach Based on Dolton partial pressure law and oil gas separation process, CO2 partial pressure profile calculating method in oil well is proposed. An experimental research was conducted on N80, 3Cr-L80 and 13Cr-L80 material. A field application practice was used. Findings It is necessary to calculate the CO2 partial pressure properly to ensure a safety and economy downhole tubing (or casing) anti-corrosion design. Originality/value The gas and oil separation theory and corrosion theory are combined together to give a useful method in downhole tubing anti-corrosion design method.

scholarly journals Predicting the Oil Well Production Based on Multi Expression Programming

2016 ◽  
Vol 9 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Xin Ma ◽  
Zhi-bin Liu
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Back Propagation ◽  
Petroleum Engineering ◽  
Reconstruction Technique ◽  
Oil Well ◽  
Empirical Methods ◽  
Well Production ◽  
Multi Expression Programming ◽  
Intelligent Methods

Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil well production, combined with the phase space reconstruction technique. The MEP has shown a better performance than the back propagation networks, gene expression programming method and the Arps decline model in the experiments, and it has also been shown that the optimal state of the MEP could be easily obtained, which could overcome the over-fitting.

scholarly journals Characterization of a Mobile Waste-Robot: a Heuristic Method to Path Planning using Artificial Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3902-3910
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Planning ◽  
Mobile Robotics ◽  
Heuristic Method ◽  
Correlation Coefficients ◽  
Path Tracking ◽  
Ann Model ◽  
Artificial Neural ◽  
Tracking Time

In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

Application of Computational Intelligence in Generating Synthetic Reservoir Rock Mechanical Parameters for Building Geo-Models.

2015 ◽  
Author(s):  
L. C. Akubue ◽  
A.. Dosunmu ◽  
F. T. Beka
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Numerical Models ◽  
Petroleum Industry ◽  
Oil Field ◽  
Wellbore Stability ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Earth Model ◽  
Sonic Log

Abstract Oil field Operations such as wellbore stability Management and variety of other activities in the upstream petroleum industry require geo-mechanical models for their analysis. Sometimes, the required subsurface measurements used to estimate rock parameters for building such models are unavailable. On this premise, past studies have offered variety of methods and investigative techniques such as empirical correlations, statistical analysis and numerical models to generate these data from available information. However, the complexity of the relationships that exists between the natural occurring variables make the aforementioned techniques limited. This work involves the application of Artificial Neural Networks (ANNs) to generating rock properties. A three-layer back-propagation neural network model was applied predicting pseudo-sonic data using conventional wireline log data as input. Four well data from a Niger-Delta field were used in this study, one for training, one for validating and the two others for generating and testing results. The network was trained with different sets of initial random weights and biases using various learning algorithms. Root mean square error (RMSE) and correlation coefficient (CC) were used as key performance indicators. This Neural-Network-Generated-Sonic-log was compared with those generated with existing correlations and statistical analysis. The results showed that the most influential input vectors with various configurations for predicting sonic log were Depth-Resistivity-Gamma ray-Density (with correlating coefficient between 0.7 and 0.9). The generated sonic was subsequently used to compute for other elastic properties needed to build mechanical earth model for evaluating the strength properties of drilled formations, hence optimise drilling performance. The models are useful in Minimizing well cost, as well as reducing Non Productive Time (NPT) caused by wellbore instability. This technique is particularly useful for mature fields, especially in situations where obtaining this well logs are usually not practicable.

Alaskan Transportation: An Overview of Some Aspects of Transporting Alaskan Crude Oil

1979 ◽  
Vol 16 (03) ◽  
pp. 211-224
Author(s):  
Stanley Factor ◽  
Sandra J. Grove
Keyword(s):  
Crude Oil ◽  
Gulf Coast ◽  
The United States ◽  
Oil Field ◽  
Oil Well ◽  
Paper Briefly ◽  
The North ◽  
Prudhoe Bay ◽  
Design Construction ◽  
Commercial Oil

The first commercial oil well in Alaska was drilled in 1901, but it was in 1968 that Alaska was thrust into prominence as an oil producer with the discovery of the Prudhoe Bay field, the largest oil field ever found in the United States. This paper briefly explores the transportation-related aspects of the design, construction, and operation of the pipeline and support facilities. The pipeline terminates at Port Valdez on Prince William Sound. It is from here that the second leg of the journey to the energy-hungry lower 48 states begins. A thoroughly modern and unique marine transportation system is being utilized to transport approximately 1.2 million barrels (191 000 m3) per day of Alaskan crude oil to West and Gulf Coast refineries. The Valdez Terminal, the pipeline, the North Slope supply, and vessel particulars and operations are discussed; in addition, environmental and legal problems are outlined.

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