Kíldare George Ramos Gurjão

Texas A&M University

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Eduardo Gildin

Texas A&M University

Mark E. Everett

Texas A&M University

Richard G. Gibson

Freedom House

Denis José Schiozer

Universidade Estadual de Campinas (UNICAMP)

J. Hohendorff

Universidade Estadual de Campinas (UNICAMP)

Cooperative Institutions

Texas A&M University

Mitchell Institute

Universidade Estadual de Campinas (UNICAMP)

Hospital de Clínicas da Unicamp

Petrobras (Brazil)

The University of Texas at Austin

Rice University

Brazilian Center for Research in Energy and Materials

Universidad Católica Cecilio Acosta

University of Petroleum

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Estimation of Far-Field Fiber Optics Distributed Acoustic Sensing DAS Response Using Spatio-Temporal Machine Learning Schemes and Improvement of Hydraulic Fracture Geometric Characterization

SPE Hydraulic Fracturing Technology Conference and Exhibition (2022)

Kíldare George Ramos Gurjão Eduardo Gildin Richard G. Gibson Mark E. Everett

Abstract Distributed Acoustic Sensing (DAS) is a fiber optics method that is revolutionizing the unconventional reservoir monitoring technology with substantial spatial coverage, high frequency data acquisition, and broad cable deployment options including hazardous/harsh environments compared to traditional geophysical methods such as point sensors (i.e., geophones). However, a single well equipped with fiber cannot acquire the far-field strain response since the sensitivity of this technique is restricted to a region near the monitor well. In this paper, we develop an Artificial Intelligence (AI) algorithm to estimate the magnitude of the far-field DAS response for any spatio-temporal input. Moreover, we identify a discontinuity in displacement results following fracture hit, which is interpreted as an effect of rock plastic deformation, and for the first time we demonstrate that it may be related to fracture width. Therefore, the output of our algorithm is used to estimate such geometric property along time in multiple locations. We generate the tangent displacement component (uy) (parallel to monitor well) using an in-house code based on Displacement Discontinuity Method (DDM). Several monitor wells are incorporated in the simulation of physical scenarios characterized by single and multiple hydraulic fractures. For each specific scenario we train and test an Artificial Neural Network (ANN) with position and time as input variables, and axial displacement as output. The Machine Learning (ML) model is designed with 7 hidden layers, 100 the maximum number of neurons per layer and hyperbolic tangent as activation function. Finally, predicted uy is used to: (1) obtain Distributed Acoustic Sensing (DAS) data deriving it sequentially in space and time; and (2) estimate fracture width based on discontinuity magnitude. Training stage is performed avoiding overfitting and minimizing ANN loss function. In the testing phase, error between true and predicted variables is negligible in the entire waterfall plot region, except at initial time steps where fracture treatment starts at operation well and magnitude of axial displacement collected at monitor well is very small on the order of 10-6 or even lower. In this case, we suspect that these tiny supervisor values may have minimal impact on the loss function, and consequently weights and biases of regression model are barely updated to consider the effect of such outputs. Regarding fracture width estimation, error reduces consistently along time at all locations reaching values near 0%. To the best of our knowledge this is the first work that creates a ML algorithm able to estimate strain fields generated during hydraulic fracturing treatments merely based on position and time inputs. The model developed with synthetic data is an incentive for the deployment of multiple monitor wells in the field to enhance beyond the near wellbore region geometric characterization of created fracture systems, and possibly identify critical patterns associated with fracture propagation that ultimately can lead to production optimization.

Discontinuity (linguistics)

Geophone

10.2118/209119-ms

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Oscillation mitigation in subsurface and surface couplings using PID controllers

Journal of Petroleum Science and Engineering (2020)

Kíldare George Ramos Gurjão Eduardo Gildin J. Hohendorff Denis José Schiozer

Oscillation (cell signaling)

10.1016/j.petrol.2020.106920

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Mechanistic Modeling of Distributed Strain Sensing DSS and Distributed Acoustic Sensing DAS to Assist Machine Learning Schemes Interpreting Unconventional Reservoir Datasets

SPE Annual Technical Conference and Exhibition (2021)

Kíldare George Ramos Gurjão Eduardo Gildin Richard G. Gibson Mark E. Everett

Abstract The use of fiber optics in reservoir surveillance is bringing valuable insights to fracture geometry and fracture-hit identification, stage communication and perforation cluster fluid distribution in many hydraulic fracturing processes. However, given the complexity associated with field data, its interpretation is a major challenge faced by engineers and geoscientists. In this work, we propose to generate Distributed Strain/Acoustic Sensing (DSS/DAS) synthetic data of a cross-well fiber deployment that incorporate the physics governing hydraulic fracturing treatments. Our forward modeling is accurate enough to be reliably used in tandem with data-driven (machine learning) interpretation methods. The forward modeling is based on analytical and numerical solutions. The analytical solution is developed integrating two models: 2D fracture (e.g. Khristianovic-Geertsma-de Klerk known as KGD) and induced stress (e.g. Sneddon, 1946). DSS is estimated using the plane strain approach that combines calculated stresses and rock properties (e.g. Young's modulus and Poisson ratio). On the other hand, the numerical solution is implemented using the Displacement Discontinuity Method (DDM), a type of Boundary Element Method (BEM), with net pressure and/or shear stress as boundary condition. In this case, fiber gauge length concept is incorporated deriving displacement (i.e. DDM output) in space to obtain DSS values. In both methods DAS is estimated by the differentiation of DSS in time. The analytical technique considers a single fracture opening and is used in a sensitivity analysis to evaluate the impact that rock/fluid parameters can promote on strain time histories. Moreover, advanced cases including multiple fractures failing in tensile or shear mode are simulated applying the numerical technique. Results indicate that our models are able to capture typical characteristics present in field data: heart-shaped pattern from a fracture approaching the fiber, stress shadow and fracture hits. In particular, the numerical methodology captures relevant phenomenon associated with hydraulic and natural fractures interaction, and provides a solid foundation for generating accurate and rich synthetic data that can be used to support a physics-based machine learning interpretation framework. The developed forward modeling, when embedded in a classification or regression artificial intelligence framework, will be an important tool adding substantial insights related to field fracture systems that ultimately can lead to production optimization. Also, the development of specific packages (commercial or otherwise) that explicitly model both DSS and DAS, incorporating the impact of fracture opening and slippage on strain and strain rate, is still in its infancy. This paper is novel in this regard and opens up new avenues of research and applications of synthetic DAS/DSS in hydraulic fracturing processes.

Distributed Acoustic Sensing

Discontinuity (linguistics)

Plane stress

10.2118/206049-ms

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Citations (1)

Investigation of Strain Fields Generated by Hydraulic Fracturing with Analytical and Numerical Modeling of Fiber Optic Response

SPE Reservoir Evaluation & Engineering (2022)

Kíldare George Ramos Gurjão Eduardo Gildin Richard G. Gibson Mark E. Everett

Summary The use of fiber optics in reservoir surveillance is bringing valuable insights into fracture geometry and fracture-hit identification, stage communication, and perforation cluster fluid distribution in many hydraulic fracturing processes. However, given the complexity associated with field data, its interpretation is a major challenge faced by engineers and geoscientists. In this work, we propose to generate distributed strain sensing (DSS)/distributed acoustic sensing (DAS) synthetic data of a crosswell fiber deployment that incorporates the physics governing hydraulic fracturing treatments. Our forward modeling can be used to add value to the interpretation task. The forward modeling is based on analytical and numerical solutions. The analytical solution is developed integrating two models: 2D fracture (e.g., Khristianovic-Geertsma-de Klerk known as KGD) and Sneddon’s induced stress. DSS is estimated using the plane strain approach that combines calculated stresses and rock properties (e.g., Young’s modulus and Poisson’s ratio). On the other hand, the numerical solution is implemented using the displacement discontinuity method (DDM), a type of boundary element method, with net pressure and/or shear stress as the boundary condition. In this case, the fiber gauge length concept is incorporated deriving displacement (i.e., DDM output) in space to obtain DSS values. In both methods, DAS is estimated by the differentiation of DSS in time. The analytical technique considers a single fracture opening and is used in a sensitivity analysis to evaluate the impact that rock/fluid parameters can promote on strain time histories. Moreover, advanced cases including multiple fractures failing in tensile or shear mode are simulated applying the numerical technique. Results indicate that our models are able to capture typical characteristics present in field data: heart-shaped pattern from a fracture approaching the fiber, stress shadow, and fracture hits. In particular, the numerical methodology captures relevant phenomenon associated with hydraulic and natural fractures interaction, which is often interpreted purely in terms of opening fractures. We can anticipate that the developed forward modeling, when embedded in a classification or regression artificial intelligence framework, will be an important tool adding substantial insights related to field fracture systems that ultimately can lead to production optimization. Also, the development of specific packages (commercial or otherwise) that explicitly model both DSS and DAS, incorporating the impact of fracture opening and slippage on strain and strain rate is still in its infancy. This paper is novel in this regard and opens up new avenues of research and applications of synthetic DAS/DSS in hydraulic fracturing processes.

Discontinuity (linguistics)

Geomechanics

10.2118/206049-pa

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Citations (7)