Geophysical helicopter-based magnetic methods for locating wells

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B269-B279 ◽  
Author(s):  
Vladislav Kaminski ◽  
Richard W. Hammack ◽  
William Harbert ◽  
Garret A. Veloski ◽  
James Sams ◽  
...  
Keyword(s):  
Magnetic Susceptibility ◽  
Accurate Determination ◽  
Likelihood Estimation ◽  
Vertical Gradient ◽  
Magnetic Data ◽  
Small Subset ◽  
Data Sets ◽  
Successful Recovery ◽  
Tilt Derivative ◽  
Airborne Magnetic

We studied the problem of determining accurately the location of abandoned and sometimes undocumented wells and the challenging and increasingly important task related to subsurface reservoir integrity and regional economic development. We reviewed a variety of semiquantitative methods based on geophysical workflows, and we tested these with airborne magnetic data collected at two field sites. Our main conclusion is that airborne magnetic surveys represent a high-value tool to aid in the accurate determination of abandoned well locations and characteristics. At one site, two surveys were collected at slightly different altitudes to compare workflow robustness and allow the observed vertical magnetic gradient to be included in well detection workflows. We also investigated using focal zone anomaly statistics (using the magnetic field intensity and its first and second horizontal derivatives), analytic signal, tilt derivative, and calculated vertical gradient. In addition, we used a 3D inversion of a small subset of data to investigate the successful recovery of well-related magnetic susceptibility distribution and estimate subsurface well topology. The recovered magnetic susceptibility volume showed distinctive vertically elongated objects that correspond to known wells. Maximum likelihood estimation and confidence calculations were then applied to these data sets and indicated that high-confidence well locations could be determined and characterized using such airborne total magnetic data.

3-D inversions of magnetic gradiometer data in archeological prospecting: Possibilities and limitations

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 849-860 ◽  
Author(s):  
Jörg Herwanger ◽  
Hansruedi Maurer ◽  
Alan G. Green ◽  
Jürg Leckebusch
Keyword(s):  
Magnetic Susceptibility ◽  
Synthetic Data ◽  
Vertical Gradient ◽  
Magnetic Data ◽  
Data Sets ◽  
Northern Switzerland ◽  
Recorded Data ◽  
Susceptibility Contrast ◽  
Depth Extent ◽  
Archeological Site

A vertical‐gradient magnetic system based on optically pumped Cesium sensors has been used to map subtle magnetic anomalies across infilled pit houses and ditches at a medieval archeological site in northern Switzerland. For estimating the locations and dimensions of these features from the recorded data, we have designed and implemented an appropriate inversion scheme. Tests of this scheme on realistic synthetic data sets suggested that suitable minimum magnetic susceptibility contrasts and smoothing parameters for the inversion may be directly extracted from the data. Inversions with minimum magnetic susceptibility contrasts generated causative bodies with maximum plausible sizes. By using higher magnetic susceptibility contrasts, a complete suite of models that matched the data equally well was produced. To constrain better the magnetic susceptibility constrast within a selected area of the archeological site, shallow samples of topsoil and sediment were analyzed in the laboratory. An inversion based on the measured magnetic susceptibility contrast yielded reliable estimates of the locations, 3-D geometries, and sizes of two small pit houses. The depth extent of one pit house was subsequently verified by shallow drilling. We concluded that inversions of vertical‐gradient magnetic data constrained by magnetic susceptibility or shallow borehole information are rapid and inexpensive means of providing key knowledge on the depth distribution of inductively magnetized bodies.

Connecting onshore-offshore Campos Basin structures: Interpretation of high-resolution airborne magnetic data

2014 ◽  
Vol 2 (4) ◽  
pp. SJ35-SJ45 ◽  
Author(s):  
Juarez Lourenço ◽  
Paulo T. L. Menezes ◽  
Valeria C. F. Barbosa
Keyword(s):  
Source Rocks ◽  
Magnetic Data ◽  
Data Sets ◽  
Hydrocarbon Migration ◽  
Data Set ◽  
Campos Basin ◽  
Turbidite Sedimentation ◽  
Edge Detection Method ◽  
Transfer Faults ◽  
Airborne Magnetic

We interpreted northwest-trending transfer faults whose extensions are not entirely mapped in the Precambrian basement of the onshore and offshore Campos Basin. To enhance the subtle northwest–southeast lineaments not clearly seen in the total-field data, we reprocessed and merged two airborne magnetic data sets aiming at producing a single merged magnetic data set. Next, we applied a directional filter to these integrated magnetic data. Finally, we applied a multiscale edge detection method to these filtered data. This combination allowed the detection of edges and ridges that are used to produce several northwest–southeast lineations. We interpreted these northwest-trending lineations as magnetic expressions of transfer faults that cut across the onshore adjacent basement of the Campos Basin to the shallow and deep Campos Basin waters. These interpreted northwest-trending faults suggested the continuity of the known northwest-striking transfer faults in the deep Campos Basin waters toward the shallow Campos Basin waters and the adjacent continent. Moreover, our interpreted northwest-trending faults revealed the control of several known oilfields in the Campos Basin. This result supported the hypothesis of the influence of the northwest–southeast-trending transfer faults on the petroleum system of Campos Basin, which were reactivated in the Tertiary providing a pathway for the turbidite sedimentation, reworking, and redistribution of several deepwater reservoirs. In addition, it was hypothesized that this faulting system controlled the hydrocarbon migration paths from the presalt source rocks through salt windows into basal suprasalt layers.

Simultaneous 1D inversion of loop–loop electromagnetic data for magnetic susceptibility and electrical conductivity

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1857-1869 ◽  
Author(s):  
Colin G. Farquharson ◽  
Douglas W. Oldenburg ◽  
Partha S. Routh
Keyword(s):  
Magnetic Susceptibility ◽  
Objective Function ◽  
Sum Of Squares ◽  
Magnetic Data ◽  
Data Sets ◽  
Low Frequencies ◽  
Simultaneous Inversion ◽  
Electromagnetic Data ◽  
Susceptibility Effects ◽  
Logarithmic Barrier

Magnetic susceptibility affects electromagnetic (EM) loop–loop observations in ways that cannot be replicated by conductive, nonsusceptible earth models. The most distinctive effects are negative in‐phase values at low frequencies. Inverting data contaminated by susceptibility effects for conductivity alone can give misleading models: the observations strongly influenced by susceptibility will be underfit, and those less strongly influenced will be overfit to compensate, leading to artifacts in the model. Simultaneous inversion for both conductivity and susceptibility enables reliable conductivity models to be constructed and can give useful information about the distribution of susceptibility in the earth. Such information complements that obtained from the inversion of static magnetic data because EM measurements are insensitive to remanent magnetization. We present an algorithm that simultaneously inverts susceptibility‐affected data for 1D conductivity and susceptibility models. The solution is obtained by minimizing an objective function comprised of a sum‐of‐squares measure of data misfit and sum‐of‐squares measures of the amounts of structure in the conductivity and susceptibility models. Positivity of the susceptibilities is enforced by including a logarithmic barrier term in the objective function. The trade‐off parameter is automatically estimated using the generalized cross validation (GCV) criterion. This enables an appropriate fit to the observations to be achieved even if good noise estimates are not available. As well as synthetic examples, we show the results of inverting airborne data sets from Australia and Heath Steele Stratmat, New Brunswick.

A new method for determination of magnetization direction

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. L69-L73 ◽  
Author(s):  
Neal Dannemiller ◽  
Yaoguo Li
Keyword(s):  
Large Deviation ◽  
Three Dimensional ◽  
Vertical Gradient ◽  
Data Interpretation ◽  
Magnetic Data ◽  
Data Sets ◽  
Magnetization Direction ◽  
Total Magnetization ◽  
Total Gradient

The characterization and interpretation of magnetic anomalies rely upon knowledge of the total magnetization direction. Magnetization is usually assumed to consist solely, or primarily, of induced magnetization. The presence of strong remanent magnetization can alter the direction significantly and consequently adversely affect the interpretation, leading to erroneous sizes or shapes of causative bodies. Therefore, it is imperative to have some understanding of the total magnetization direction. We propose a method based upon the correlation between two quantities in magnetic data interpretation: the vertical gradient and the total gradient of the reduced-to-pole (RTP) field. This method is tested on both synthetic and field data sets. The results show that the method is effective in a variety of situations, including those with two-dimensional and three-dimensional dipping bodies and a field example that has a large deviation between the inducing field direction and the total magnetization direction.

An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi
Keyword(s):  
Magnetic Field ◽  
Magnetic Susceptibility ◽  
Least Squares ◽  
Minimization Problem ◽  
Potential Field ◽  
Field Data ◽  
Least Squares Method ◽  
Porphyry Copper ◽  
Magnetic Data ◽  
Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

Automatic 3-D interpretation of potential field data using analytic signal derivatives

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Nicole Debeglia ◽  
Jacques Corpel
Keyword(s):  
Signal Amplitude ◽  
Vertical Gradient ◽  
Analytic Signal ◽  
Magnetic Data ◽  
Practical Implementation ◽  
Potential Field Data ◽  
Gravity And Magnetic ◽  
Second Derivatives ◽  
Gravity And Magnetic Data ◽  
Derivatives Of

A new method has been developed for the automatic and general interpretation of gravity and magnetic data. This technique, based on the analysis of 3-D analytic signal derivatives, involves as few assumptions as possible on the magnetization or density properties and on the geometry of the structures. It is therefore particularly well suited to preliminary interpretation and model initialization. Processing the derivatives of the analytic signal amplitude, instead of the original analytic signal amplitude, gives a more efficient separation of anomalies caused by close structures. Moreover, gravity and magnetic data can be taken into account by the same procedure merely through using the gravity vertical gradient. The main advantage of derivatives, however, is that any source geometry can be considered as the sum of only two types of model: contact and thin‐dike models. In a first step, depths are estimated using a double interpretation of the analytic signal amplitude function for these two basic models. Second, the most suitable solution is defined at each estimation location through analysis of the vertical and horizontal gradients. Practical implementation of the method involves accurate frequency‐domain algorithms for computing derivatives with an automatic control of noise effects by appropriate filtering and upward continuation operations. Tests on theoretical magnetic fields give good depth evaluations for derivative orders ranging from 0 to 3. For actual magnetic data with borehole controls, the first and second derivatives seem to provide the most satisfactory depth estimations.

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