Stratigraphic Characterization of Reservoirs Through Segmentation of Digital Images of Well Cores from Machine Learning
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STRATIGRAPHIC CHARACTERIZATION OF RESERVOIRS THROUGH SEGMENTATION OF DIGITAL IMAGES OF WELL CORES FROM MACHINE LEARNING Thesis dissertation to obtain the degree of: Geoscientist By Juan Camilo Burgos Florez Director Ph.D. Roderick Perez Altamar Co director Ph.D. Jillian Pearse UNIVERSIDAD DE LOS ANDES SCIENCE FACULTY GEOSCIENCE DEPARTMENT Bogota, Colombia November 2018 _________________________________ Jillian Pearse _________________________________ Roderick Perez Altamar _________________________________ Juan Camilo Burgos Florez TABLE OF CONTENTS DEDICATION i ACKNOWLEDGMENTS ii ABSTRACT iii RESUMEN iv 1. INTRODUCTION 1 2. TECTONIC FRAMEWORK, REGIONAL GEOLOGY AND PETROLEUM GEOLOGY 3 2.1. Tectonic framework 3 2.2. Regional geology 5 2.2.1. La Luna formation 7 2.2.2. Cumbre formation 7 2.2.3. Umir formation 8 2.2.4. Paja formation 8 2.2.5. Tablazo formation 8 2.2.6. Simiti formation 9 2.2.7. Mugrosa formation 9 2.2.8. Cimarrona formation 9 2.3. Petroleum geology 10 3. METHODOLOGY 11 3.1. Cores description 11 3.1.1. Well X 11 3.1.2. Well Y 14 3.1.3. Well Z 16 3.2. Algorithm 21 3.2.1. Convolutional network 21 3.2.1.1. Convolution 21 3.2.1.2. Pooling 23 3.2.1.3. Flatten 24 3.2.1.4. Dropout 24 3.2.2. Classifier 25 3.2.3. Optimizer 26 3.2.4. Model 27 3.2.5. Training phase 29 3.2.5.1. Feedforward training 29 3.2.5.2. Backpropagation error 30 3.2.5.3. Weight adjustment 31 4. RESULTS 33 5. DISCUSSION 42 6. CONCLUSION 44 BIBLIOGRAPHY 45 INDEX OF FIGURES Figure 1. Localization and boundaries of the studied area. 4 Figure 2. Generalized stratigraphic column of the Middle Magdalena Valley Basin. 6 Figure 3. Location of the worked wells, well X, well Y and well Z. 11 Figure 4. Stratigraphy of well X with a gap between 4504 feet and 4299 feet. 13 Figure 5. Stratigraphy column of the well Y with a serie of gaps between 5788- 5814 feet, 5817-5861 feet,5864-5902 feet, 5904-6028 feet, 6033-6261 feet and 6262-7078 feet. 15 Figure 6.A Stratigraphy column of the well Z. 17 Figure 6.B Stratigraphy column of the well Z. 18 Figure 7.A. The matrix A represents the information of the input picture 5x5, the matrix B represents the characteristics, in other words the kernel 3x3. 22 Figure 7.B. Graphic process of the convolution, where both matrices are multiplied, and it is sum the results of each box. So, they are denominated convolved feature. 22 Figure 8. General idea of max-pooling into an algorithm. 24 Figure 9. Internal structure of a multilayer model. 28 Figure 10. Example of backpropagation model. 29 Figure 11. Process of feedforward training. 30 Figure 12. Process of backpropagation error. 32 Figure 13. Process of weights adjustment. 33 Figure 14. Graph of loss value. Training phase in the first one and validation phase in the second one. 34 Figure 15. Graphs of loss value in each loss component of the training phase. 35 Figure 16. Graphs of loss value in each loss component of the validation phase. 36 Figure 17. 0.1 feet of image of well cores segmented. Well X 37 Figure 18. Image segmentation of well core Sandstone and conglomerate with hydrocarbons were identified. 37 Figure 19. Correlation of image segmentation of well core with the stratigraphic column 38 Figure 20. 0.1 feet of image of well cores segmented. Well Y 39 Figure 21. Image segmentation of well core. Sandstone was identifying. 39 Figure 22. Correlation of image segmentation of well core with the stratigraphic column 40 Figure 23. New well core image of a new well. 40 Figure 24. 0.1 feet of image of well cores segmented. New well 41 Figure 25. Image segmentation of well core. Sandstone was identified. Figure 26. Stratigraphic correlation from electric register like the Spontaneous Potential (SP) 42 INDEX OF EQUATIONS Equation 1. Classifier 25 Equation 2. Optimizer 26 Equation 3. Backpropagation errors 31 i DEDICATION To God For give me the strength and the knowledge to make this work and allow me to overcome all the adversities. Honor and glory be to him. This work is thanks to him. To my family For supporting me in all the process and to be present when I needed help. To my mom because she is my inspiration to keep going and to my dad that advised me in all the moments that I needed it. I love you both. To my grandmother Herminda and my aunt Elda for always watching and taking care of me. To my sisters Maria Paula, Letizia and Juanita, you are my reason to continue and I hope to teach you that everything in life can be achieved with the help of God. I love you. ii ACKNOWLEDGMENTS I want to acknowledge all the institutions and people that contributed to this work. Particularly to my directors Roderick Perez and Jillian Pearse to give me all the advices and recommendations, also, to be present during the entire work. Furthermore, to the Servicio Geologico Colombiano for supplying all the information required to realize this work and giving all the support in my thesis with tips, questions, help of geologists and petroleum engineers. Likewise, to I Oil and Gas Summit for giving me the opportunity to expose my thesis as a poster and to have the privilege to receive suggestions and recommendations of big companies, geologist and petroleum engineers. Moreover, to Rigoberto Blandon for being part of the process of obtaining information and Maria Paula Castañeda for helping me with the written part. Additionally, to my family because they are the inspiration to make this work. All my friends, especially to David Angulo, Sebastian Duran, Felipe Uribe, Daniel Diaz, Mateo Rueda, Nathalia Bernal, Valentina Blandon and Mariana Sanchez that contributed in different moments of the process and supported me through difficult times. I appreciate you all very much and once again thanks for everything! iii ABSTRACT The purpose of the present work is to generate an algorithm that allows an automatic segmentation of images of well cores from machine learning. The motivation for the use of this type of analysis is to look for an application of neural networks in the petroleum industry, as a support tool for geologists that allows to specify the descriptions of possible structures and patterns that are not identified by the human eye. Whence, it was used information of three wells of the Middle Magdalena Valley Basin to know the lithologies and stratigraphic sequence to do a core description. Then, they were recollected 1966 images of core with a size of 0.1 feet to training the algorithm. So, 983 images were used to feed the training phase, and the rest to feed the validation phase with the aim to realize a supervised training. The final results were a training loss value of 1.0709 and a validation loss value of 1.0804, which shows that the images had a good segmentation but present some mistakes that are product of different aspects that are mention in the discussion. Thus, this research work will be divided into three parts: the first part will be the description of the geological context of the study area and description of the images of the cores. The second part will be the generation of codes and creation of algorithms for the segmentation of the images. The third part, will be to prove the algorithm for the segmentation of cores not used in the training and validation phase. iv RESUMEN El propósito del presente trabajo es generar un algoritmo que permita una segmentación automática de imágenes de núcleos de pozo mediante machine learning. La motivación es buscar una aplicación de redes neuronales en la industria del petróleo como una herramienta de apoyo para los geólogos que permita especificar las descripciones de posibles estructuras y patrones que no se identifican. Por lo tanto, se utilizó la información de tres pozos de la Cuenca del Valle del Magdalena Medio para conocer la secuencia estratigráfica para su descripción. Despues, se recolectaron 1966 imágenes de núcleo con un tamaño de 0.1 pies para entrenar el algoritmo. De ahi, se utilizaron 983 imágenes para alimentar la fase de entrenamiento, y el resto para alimentar la fase de validación. Los resultados finales fueron un valor de pérdida de entrenamiento de 1.0709 y un valor de pérdida de validación de 1.0804, que muestra que las imágenes tuvieron una buena segmentación, pero presentan algunos errors en donde se analizaran en la discusión. Por lo tanto, este trabajo de investigación se dividirá en tres partes: la primera será la descripción del contexto geológico del área de estudio y la descripción de las imágenes de los núcleos. La segunda parte será la generación de códigos y la creación de algoritmos para la segmentación de las imágenes. La tercera parte, será probar el algoritmo para la segmentación de núcleos no utilizados en la fase de entrenamiento y validación. 1 I. INTRODUCTION The present research work shows the new advances in machine learning and neural networks in the image processing. Since, the main characteristic of this process is that “it has been used in a wide range of fields, including in medical diagnostics, stock exchange, robot control, transportation, aviation, heavy industries, toys and games” (Hernández,2017). This technology is based on “how humans can learn by simply observing the world and formulating consistent explanation” (Zhengqi, 2018). Therefore, the purpose is to implement and to apply those concepts in the stratigraphic characterizations of reservoirs through the use of core images and establish their correlation with the stratigraphic column.