Abstract
This invention relates to a method and system for the three-dimensional reconstruction of material properties of a target using remotely located physical sensors using deep learning artificial intelligence. Utilizing the special technique disclosed here, the method provides an order of magnitude improvement in computational speed and memory requirements over current state-of-the-art artificial intelligence-based systems. The improvement in accuracy and resolution obtained enables high resolution imaging of buried infrastructure using above ground sensors mounted on drones and other devices. This makes it feasible to perform several first order inspection tasks related to pipeline health, corrosion, integrity, and others that are currently only possible using inline inspection tools such magnetic flux leakage (MFL) and ultrasonic (UT) sensors or via deployment of fiber optic sensors.
Claims
1. A novel physics-based formulation of the input data from remote sensing imaging sensors that enable the deployment of one-dimensional or low valued two-dimensional vector based deep machine learning architectures for multidimensional (3D & 4D) image reconstruction tasks and solving of inverse problems.
2. Enable the solution of claim 1) for both structured and unstructured mesh.
3. The resulting image from claim 1) and claim 2) is suitable for performing several interpretation monitoring, and reconnaissance tasks on subsurface pipelines that are typically performed by inline inspection devices in both onshore and offshore under water settings.
4. Imaging from claim 1) and claim 2) is suitable for identification of metallic objects located in the subsurface that could potential impede or cause safety hazards for development and construction projects.
5. The resulting image from claim 1) and claim 2) is also suitable to identify pipeline intersections as well as unknown abandon pipelines prior to new pipeline construction or repair of existing pipelines.
6. Imaging from claim 1) and claim 2) can assist in locating subsurface pipeline depth as well as pipelines that may be located beneath an existing pipeline sometimes run in line with one above another.
7. The resulting image from claim 1) and claim 2) can be used prior to traditional inline inspection devices to target areas of concern by identification of areas of corrosion and wall weakness.
8. Imaging from claim 1) and claim 2) supports the detection and location of leaks and breaches in pipeline integrity.
Description
BRIEF DESCRIPTION OF THE DRAWINGS AND FIGURES
[0011] FIG. 1. A simplified sketch of a drone based geophysical data acquisition system for acquiring data over buried pipelines and other infrastructure in an oilfield setting.
[0012] FIG. 2. A schematic representation of a conventional deep machine learning architecture for reconstructing 2-D and 3-D images and/or material property inversion using remote sensors.
[0013] FIG. 3. A schematic representation of deep machine learning architecture for reconstructing 3-D images and/or material property inversion using remote sensors using 1-D vector basis functions only.
[0014] FIG. 4. a. Map showing survey location for the Washington-on-Brazos case study in Texas. b. Orientation and extent of surveyed area. c. Handheld magnetometers used in the survey.
[0015] FIG. 5. a. Line depicting the vertical cross section right across the heart of the anomalies observed in the absolute amplitude map. b. Conventional least squares inversion results displayed in vertical cross section right across the heart of the 4 circular anomalies observed in FIG. 5a. c. The depth, and susceptibility distribution of the pipe is delineated and much more clearly visualized relative to conventional least squares inversion in FIG. 5.b.
[0016] FIG. 6. Threshold (0.01-0.06) value of normalized absolute susceptibility values from deep learning AI inversion. Smoothing applied for visual clarity. The reconstructed 3D image of the pipe like structure is enhanced using Al based inversion.
DETAILED DESCRIPTION OF THE INVENTION
[0017] FIG. 1 shows the general scheme for acquiring above ground geophysical sensor data using an airborne device (1) like drone. While being shown for illustrative purposes, such data can be collected by multiple other means, including but not limited to helicopters, airplanes, ground borne vehicles, handheld devices, as also towed by boat, as a submarine device, for subsea pipelines amongst others. The collected sensor data is processed to remove the influence of above ground metallic infrastructure (4 in FIG. 1) and the influence of the deeper subsurface geology (depicted by 2). The residual field is trained various potential buried subsurface pipe location, geometry, and states of material property such as magnetic susceptibility, electrical conductivity, sonic/ultrasonic velocities, amongst others to determine optimal location, geometry, and effective material property distribution to infer pipeline health, integrity, and other issues.
[0018] Referring to FIG. 2, a simple generic training architecture for current state-of-the-art deep multi-layer machine learning algorithm is shown for illustrative purposes. The input data, fed in the form of an N?R matrix, where N>1 and R?1, present in the first layer depicted as 5, is processed by a set of mathematical operators present in the first hidden layer, depicted as 6, and its output matrix whose shape is P?Q sent to the second hidden layer, depicted as 7, wherein the shape of the output matrix is transformed to J?K?L. Eventually, these transformations will result in an output matrix whose dimensions will be the same as the desired output image (U?V?W). Based on the differences between the pixel values of the output matrix and those images used as ground truth for training, the system will continue to iterate until the difference between the pixel values of the predicted image and the ground truth are below a certain predetermined threshold and/or subsequent iterations do not alter this difference much.
[0019] In FIG. 3, the modification to this approach is discussed. The adjoint operator can be used to reproject the input data 10 to the same dimensions as the output image matrix 15 and recast as a vector, 11. Now, all the processing steps (12-15) are simplified as vector operations instead of matrices which reduce the overall computational footprint by about an order of magnitude.
[0020] While either approach is suitable for handling buried subsurface infrastructure like pipelines, the embodiment discussed in FIG. 3 is more efficient.
[0021] A practical demonstration of the method using data acquired by Texas A & M university students under the guidance of Prof. Mark Everett using handheld magnetic sensors is disclosed. FIG. 4a shows the location of Washington-on-Brazos State historic site where the data was acquired. FIG. 4b shows the dimensions of the field survey area and FIG. 4c shows the illustration of the magnetic sensor used for the data acquisition.
[0022] After suitable processing of data as discussed above, the results of inversion using conventional least squares method is shown in FIG. 5b and the corresponding values of relative susceptibility distribution using deep learning artificial intelligence is shown in FIG. 5c. For ease of comparison, both FIGS. 5b. and 5c. are displayed using the same color scale range. While the conventional inversion hints at the presence of a buried pipe somewhere between 0-3 m below the ground surface, the relative changes in susceptibility distribution results from the Al based inversion demonstrates a confident top of pipe at 1-1.5 m below ground surface with variation in susceptibility along pipe axis suggestive of changes in thickness, corrosion, and other issues, implying a conservative estimated 6-fold improvement.
[0023] In FIG. 6, a threshold (0.01-0.06) 3D distribution of the absolute values of relative susceptibility distribution are shown. The full 3D geometry of the pipe, its susceptibility and potential thickness variations are now visible. Such information is not easily inferenced otherwise.
[0024] While the present invention has been described in terms of particular embodiments and applications, in both summarized and detailed forms, it is not intended that these descriptions in any way limit its scope to any such embodiments and applications, and it will be understood that many substitutions, changes and variations in the described embodiments, applications and details of the method and system illustrated herein and of their operation can be made by those skilled in the art without departing from the spirit of this invention.
