Implementation of Tikhonov Regularization in Oil Well Liquid Level Reading Data

In the petroleum industry, it is common practice to do survey well liquid level for monitoring well in the purpose of evaluation well production capacity, for setting performance of downhole pump. Here we proposed liquid level survey using acoustic well sounder (echosounder) equipment. The reading of liquid level in the oil well is contained noises due to some physical and mechanical condition. An idea to handle large scattered field data contains noises is smoothness method by Tikhonov regularization.Liquid level survey is set up under shunt in well condition, to clearly monitoring liquid level rises in the well column. So, we have acquired field data reading. Beside the field data reading using echosounder tool, we also need to calculate expected liquid level, because that noises factor in the field data. The equation is generated to define calculation of liquid level increases in the well column as a function of time. The initial condition of started liquid column height h0 and well production rate Q0 at t=0 is definite. We build the Volterra integral equation of the 1st kind for this calculation purpose.The ill-posed problem performs in the data, needs the solution for smoothness. Tikhonov regularization (Least Squared problem) has handling this problem. Some value of regularization parameter were employed to the calculation.This paper is an innovative idea to maximum utilization of fluid level data monitoring in the well, while the acquired data is scattered or contains error. After smoothness of the data, qualified model solution curve is fully advantage for well interpretation.

Download Full-text

An adaptive iterative method for downward continuation of potential-field data from a horizontal plane

Geophysics ◽

10.1190/geo2012-0404.1 ◽

2013 ◽

Vol 78 (4) ◽

pp. J43-J52 ◽

Cited By ~ 23

Author(s):

Xiaoniu Zeng ◽

Xihai Li ◽

Juan Su ◽

Daizhi Liu ◽

Hongxing Zou

Keyword(s):

Iterative Method ◽

Tikhonov Regularization ◽

Potential Field ◽

Horizontal Plane ◽

Field Data ◽

Regularization Parameter ◽

Downward Continuation ◽

Tikhonov Regularization Method ◽

Potential Field Data ◽

Wavenumber Domain

We have developed an improved adaptive iterative method based on the nonstationary iterative Tikhonov regularization method for performing a downward continuation of the potential-field data from a horizontal plane. Our method uses the Tikhonov regularization result as initial value and has an incremental geometric choice of the regularization parameter. We compared our method with previous methods (Tikhonov regularization, Landweber iteration, and integral-iteration method). The downward-continuation performance of these methods in spatial and wavenumber domains were compared with the aspects of their iterative schemes, filter functions, and downward-continuation operators. Applications to synthetic gravity and real aeromagnetic data showed that our iterative method yields a better downward continuation of the data than other methods. Our method shows fast computation times and a stable convergence. In addition, the [Formula: see text]-curve criterion for choosing the regularization parameter is expressed here in the wavenumber domain and used to speed up computations and to adapt the wavenumber-domain iterative method.

Download Full-text

A modified Tikhonov regularization method based on Hermite expansion for solving the Cauchy problem of the Laplace equation

Open Mathematics ◽

10.1515/math-2020-0111 ◽

2020 ◽

Vol 18 (1) ◽

pp. 1685-1697

Author(s):

Zhenyu Zhao ◽

Lei You ◽

Zehong Meng

Keyword(s):

Cauchy Problem ◽

Laplace Equation ◽

Tikhonov Regularization ◽

Regularization Method ◽

Regularization Parameter ◽

Solution Process ◽

Tikhonov Regularization Method ◽

Hermite Expansion ◽

The Cauchy Problem ◽

Ill Posed

Abstract In this paper, a Cauchy problem for the Laplace equation is considered. We develop a modified Tikhonov regularization method based on Hermite expansion to deal with the ill posed-ness of the problem. The regularization parameter is determined by a discrepancy principle. For various smoothness conditions, the solution process of the method is uniform and the convergence rate can be obtained self-adaptively. Numerical tests are also carried out to verify the effectiveness of the method.

Download Full-text

Prediction of oil well production based on the time series model of optimized recursive neural network

Petroleum Science and Technology ◽

10.1080/10916466.2021.1877303 ◽

2021 ◽

pp. 1-10

Author(s):

Yuefei Cheng ◽

Yang Yang

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Model ◽

Oil Well ◽

Well Production ◽

Recursive Neural Network

Download Full-text

A New Hybrid Inversion Method for 2D Nuclear Magnetic Resonance Combining TSVD and Tikhonov Regularization

Journal of Imaging ◽

10.3390/jimaging7020018 ◽

2021 ◽

Vol 7 (2) ◽

pp. 18

Author(s):

Germana Landi ◽

Fabiana Zama ◽

Villiam Bortolotti

Keyword(s):

Nuclear Magnetic Resonance ◽

Magnetic Resonance ◽

Tikhonov Regularization ◽

Large Scale ◽

Regularization Parameter ◽

High Sensitivity ◽

Inversion Method ◽

Nmr Relaxometry ◽

Value Decomposition ◽

Nuclear Magnetic

This paper is concerned with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) relaxometry. This is a large-scale and ill-posed inverse problem with many potential applications in biology, medicine, chemistry, and other disciplines. However, the large amount of data and the consequently long inversion times, together with the high sensitivity of the solution to the value of the regularization parameter, still represent a major issue in the applicability of the NMR relaxometry. We present a method for two-dimensional data inversion (2DNMR) which combines Truncated Singular Value Decomposition and Tikhonov regularization in order to accelerate the inversion time and to reduce the sensitivity to the value of the regularization parameter. The Discrete Picard condition is used to jointly select the SVD truncation and Tikhonov regularization parameters. We evaluate the performance of the proposed method on both simulated and real NMR measurements.

Download Full-text

Disadvantages in the Use of Acid to Increase Oil Well Production

Science ◽

10.1126/science.87.2266.10-u ◽

1938 ◽

Vol 87 (2266) ◽

pp. 10-12

Keyword(s):

Oil Well ◽

Well Production

Download Full-text

Applied Mechanics Problems in the Oil and Gas Industry

Applied Mechanics Reviews ◽

10.1115/1.3149511 ◽

1986 ◽

Vol 39 (11) ◽

pp. 1687-1696 ◽

Cited By ~ 2

Author(s):

Jean-Claude Roegiers

Keyword(s):

Oil And Gas ◽

Petroleum Industry ◽

Oil Industry ◽

Oil And Gas Industry ◽

Production Equipment ◽

Paper Briefly ◽

Research Activity ◽

Gas Industry ◽

Well Production ◽

Current Research Activity

The petroleum industry offers a broad spectrum of problems that falls within the domain of expertise of mechanical engineers. These problems range from the design of well production equipment to the evaluation of formation responses to production and stimulation. This paper briefly describes various aspects and related difficulties with which the oil industry has to deal, from the time the well is spudded until the field is abandoned. It attempts to delineate the problems, to outline the approaches presently used, and to discuss areas where additional research is needed. Areas of current research activity also are described; whenever appropriate, typical or pertinent case histories are used to illustrate a point.

Download Full-text

Predicting the Oil Well Production Based on Multi Expression Programming

The Open Petroleum Engineering Journal ◽

10.2174/1874834101609010021 ◽

2016 ◽

Vol 9 (1) ◽

pp. 21-32 ◽

Cited By ~ 2

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.

Download Full-text

The Regularization Method Based on Complex Collinearity Diagnostics and Metrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.416-417.1393 ◽

2013 ◽

Vol 416-417 ◽

pp. 1393-1398

Author(s):

Chao Zhong Ma ◽

Yong Wei Gu ◽

Ji Fu ◽

Yuan Lu Du ◽

Qing Ming Gui

Keyword(s):

Tikhonov Regularization ◽

Regularization Method ◽

Regularization Parameter ◽

Measurement Data ◽

Tikhonov Regularization Method ◽

Parameter Determination ◽

Selection Methods ◽

Ill Posed ◽

Collinearity Diagnostics ◽

Regularization Matrix

In a large number of measurement data processing, the ill-posed problem is widespread. For such problems, this paper introduces the solution of ill-posed problem of the unity of expression and Tikhonov regularization method, and then to re-collinearity diagnostics and metrics based on proposed based on complex collinearity diagnostics and the metric regularization method is given regularization matrix selection methods and regularization parameter determination formulas. Finally, it uses a simulation example to verify the effectiveness of the method.

Download Full-text

2013 offshore petroleum exploration acreage release

The APPEA Journal ◽

10.1071/aj12006 ◽

2013 ◽

Vol 53 (1) ◽

pp. 63

Author(s):

Tania Constable

Keyword(s):

Capital Expenditure ◽

Petroleum Industry ◽

Production Capacity ◽

Rapid Expansion ◽

Petroleum Exploration ◽

Production Models ◽

Investment Opportunities ◽

Trading Partners ◽

Under Construction ◽

Offshore Petroleum

Exploration is essential for the future of Australia’s resources sector, to enhance our international competitiveness and ensure the long-term growth of this important industry while maintaining Australian energy security and that of our major energy trading partners. Encouraging investment in offshore petroleum exploration is facilitated though the annual Offshore Petroleum Exploration Acreage Release prepared in collaboration between the Department of Resources, Energy and Tourism, and Geoscience Australia. The annual release is underpinned by a stable economic environment, and a regulatory framework that provides the industry with a variety of investment opportunities. Australia has abundant natural gas reserves and is experiencing a rapid expansion of its LNG production capacity. Today, Australia is the world’s fourth-largest exporter of LNG, with a total export capacity of 24.3 million tonnes per annum from its three operational projects. Capacity will further increase to around 80 million tonnes per annum in 2017 once the seven projects presently under construction come online. These projects represent more than US$175 billion in capital expenditure announced since mid-2007, and result in Australia becoming the only country to use three LNG production models: conventional offshore gas with onshore LNG production; FLNG production; and, CSG-based LNG production. This paper will provide detail about the acreage included in the 2013 Offshore Petroleum Exploration Acreage Release. Areas are carefully selected to offer the global petroleum industry a variety of investment opportunities. This paper will also discuss the supporting regulatory environment and new government initiatives, including the introduction of a five-year exploration strategy for acreage release and the introduction of a cash bidding system as part of future offshore petroleum acreage releases.

Download Full-text