Fault Identification for the Purposes of Evaluating the Risk of Induced Seismicity: A Novel Application of the Flowback DFIT

Zeinabady, D.; Clarkson, Christopher R.; Razzaghi, S.; Haqparast, Sadjad; Benson, Abdul-Latif L.; Azad, Mohammad

doi:10.2118/211100-pa

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…Except for the initial pore pressure, which varies by distance from the fault core as shown in figure 5, other input parameters are uniform. Table 2 summarizes the model parameters used for this simulation, which are modified from Zeinabady et al [42] to ensure consistency with the steady-state model parameters used here. The simulation is isothermal, with a uniform temperature of 82°C set throughout the model.…”

Section: (A) Methodsmentioning

confidence: 99%

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Sealing faults and fluid-induced seismicity

Esmaeilzadeh,

Eaton,

Hosseini

et al. 2024

Phil. Trans. R. Soc. A.

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Sealing faults are nearly impermeable barriers that can form boundaries between subsurface pore-pressure domains. In hydrocarbon systems, sealing faults commonly form part of a structural trap; they are thus important elements for future storage of CO 2 and other gases in depleted reservoirs. The Triassic Montney Formation in western Canada hosts low-permeability gas reservoirs containing sealing faults that have previously been assumed to compartmentalize pressure domains. In this study, we show that the distribution of induced seismicity associated with hydraulic fracturing (HF) exhibits a statistically significant spatial correlation with zones of high lateral gradient in pore pressure. These high-gradient zones are interpreted as sealing fault systems. The largest induced seismicity sequence, including a 4.5 M L mainshock on 30 November 2018, occurred during HF treatments in two horizontal wells, between which there is an exceptionally large contrast (~10 MPa) in measured pore pressure. Numerical simulation of a simplified model of a hydraulic fracture intersecting a nearby vertical fault, followed by fault rupture using rate-and-state friction rheology, generates results that are in good agreement with observed strike-slip faulting near one of the HF wells. Our study demonstrates that sealing faults exhibit previously unrecognized behaviour that may be important for understanding induced seismicity risk. This article is part of the theme issue ‘Induced seismicity in coupled subsurface systems’.

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Section: (A) Methodsmentioning

confidence: 99%

“…Table 2 summarizes the model parameters used for this simulation, which are modified from Zeinabady et al . [42] to ensure consistency with the steady-state model parameters used here. The simulation is isothermal, with a uniform temperature of 82°C set throughout the model.…”

Section: Methodsmentioning

confidence: 99%

Sealing faults and fluid-induced seismicity

Esmaeilzadeh,

Eaton,

Hosseini

et al. 2024

Phil. Trans. R. Soc. A.

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“…[48] applied ML algorithms to classify seismic driven reservoirs. [49] employed ML and IoT for generation of early warnings before and occurrence of an earthquake. [50] applied deep learning methods to study induced seismicity impact caused by artificial loading by man-made objects.…”

Section: Computational Approachesmentioning

confidence: 99%

Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

Tehseen,

Uzma Omer,

Maham Mehr Awan

et al. 2024

VFAST trans. softw. eng.

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In this article, impact of climatic anomalies and artificial hydraulic loading on earthquake generation has been studied using federated learning (FL) technique and a model for the prediction of earthquake has been proposed. Federated Learning being one of the most recent techniques of machine learning (ML) guarantees that the proposed model possesses the intrinsic ability to handle all concerns related to data involving data privacy, data availability, data security, and network latency glitches involved in earthquake prediction by restricting data transmission to the network during different stages of model training. The main objective of this study is to determine the impact of artificial stresses and climatic anomalies on increase and decrease in regional seismicity. Experimental verification of proposed model has been carried out within 100 km radial area from 34.708o N, 72.5478o E in Western Himalayan region. Regional data of atmospheric temperature, air pressure, rainfall, water level of reservoir and seismicity has been collected on hourly bases from 1985 till 2022. In this research, four client stations at different points within the selected area have been established to train local models by calculating time lag correlation between multiple data parameters. These local models are transmitted to central server where global model is trained for generating earthquake alert with ten days lead time alarming a specific client that reported high correlation among all selected parameters about expected earthquake.

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A Hybrid Analytical and Data-Driven Approach for Improved Prediction of Recovery Performance in Tight Oil Reservoirs

Moussa,

Dehghanpour

2023

SPE Journal

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Summary The heterogeneity of tight reservoirs, along with their complex geologic characteristics and the diverse completion practices used, presents challenges in developing accurate models to forecast the productivity for multifractured horizontal wells (MFHWs) completed in these reservoirs. This paper introduces a new early-time diagnostic tool that leverages early-time two-phase flowback data to forecast long-term productivity and evaluate completion efficiency. To achieve this, two novel models were developed. The first model, the water/oil-ratio model (WORM), uses a hybrid analytical and data-driven approach to describe the observed log-linear relationship between water/oil ratio (WOR) and load recovery (amount of fracturing water produced back after hydraulic fracturing operations) as an analogy to the log-linear relationship between the water/oil relative permeability ratio and water saturation. Next, a neural network is used to couple WORM parameters with key petrophysical properties to analyze the impact of fracture and formation properties on WOR performance, predict WOR as a function of load recovery, forecast ultimate load recovery, and estimate effective fracture volume and initial water saturation in fracture. The second model, the cumulative oil production model (COPM), is a data-driven model that predicts oil production as a function of load recovery during the matrix-dominated flow regime. The application of WORM and COPM on Niobrara and Codell formation wells showed that Codell wells generally exhibit better load recovery and larger effective fracture volume compared with Niobrara wells, but both formations exhibit similar oil recovery performance, indicating independent flow regimes within the effective fractures. The effective fracture volume estimated by WORM was validated against the estimated volume from recorded microseismic events. The results also showed that using the same completion practice to achieve a similar effective fracture volume in child wells does not necessarily lead to similar oil productivity. This paper introduces a holistic workflow that links early two-phase flowback data with well productivity and completion efficiency and is anticipated to aid petroleum engineers in optimizing hydraulic fracturing operations.

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Fault Identification for the Purposes of Evaluating the Risk of Induced Seismicity: A Novel Application of the Flowback DFIT

Cited by 3 publications

References 46 publications

Sealing faults and fluid-induced seismicity

Sealing faults and fluid-induced seismicity

Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

A Hybrid Analytical and Data-Driven Approach for Improved Prediction of Recovery Performance in Tight Oil Reservoirs

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