Macro Insights from Interval Pressure Transient Tests: Deriving Key Near-Wellbore Fracture Parameters in a Light Oil Reservoir Offshore Norway

Freites, Alfredo; Corbett, Patrick William Michael; Geiger, Sebastian; Norgard, Jens-Petter

doi:10.2118/195435-ms

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Other input parameters used to set up the numerical simulation case are summarised in Table 2. We compared our simulation model against well-known analytical solutions, the radial homogeneous models (Earlougher, 1977) and the infinite wellbore-intersecting fractures models (Gringarten, 1974), following the methodology presented in Freites et al (2019) and Egya et al (2019aEgya et al ( , 2019b to ensure that simulation results are free from numerical artefacts arising from the Manuscript submitted to Transport in Porous Media fractures projection onto the simulation grid or the boundary effects due to the assumption of symmetry.…”

Section: Numerical Simulations To Compute 𝒑𝒑' Curvesmentioning

confidence: 99%

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Freites¹,

Corbett²,

Rongier

et al. 2022

Preprint

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Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure Transient Analysis (PTA) could play a key role for fracture characterization purposes if proper links are built between the pressure derivative responses () and the fracture properties; however, the PTA process is particularly challenging in the presence of fractures because they can manifest themselves in many different . Our work aims at providing a tool for effectively handling the diversity in fracture-induced by automatically classifying them and identifying characteristic patterns in the data. To represent this diversity of in naturally fractured reservoirs, we created a synthetic dataset from numerical simulation that comprised 2560 , corresponding to 10 stochastic realizations of 256 combinations of different fracture intensities and average fracture apertures, organized in two orthogonal fracture sets. To group the , we developed an unsupervised maching learning approach that can distinguish beyond the variable temporality of the signals by leveraging the dynamic time warping algorithm in a k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the first pressure derivatives if the second pressure derivatives are used as the classification variable. The analysis of the Naturally Fracture Reservoirs dataset indicated that 12 clusters were appropriate to describe the full collection of . We suggest that the respective cluster medoids can be regarded as reference curves for various Naturally Fractured Reservoirs and be used as a guide in the interpretation of real well tests. The classification exercise also allowed us to identify the key geological features that influence the , namely 1) the distance from the wellbore to the closest fracture(s), 2) the local/global fracture connectivity, and 3) the local/global fracture intensity.

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Section: Numerical Simulations To Compute 𝒑𝒑' Curvesmentioning

confidence: 99%

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Freites¹,

Corbett²,

Rongier

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

et al. 2023

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Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many different p′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-related p′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560 p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in the p′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the p′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection of p′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence the p′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour.

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Calibration of naturally fractured reservoir models using integrated well-test analysis – an illustration with field data from the Barents Sea

Egya

Corbett

Geiger

et al. 2021

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This paper successfully applied the geoengineering workflow for integrated well-test analysis to characterise fluid flow in a newly discovered fractured reservoir in the Barents Sea. A reservoir model containing fractures and matrix was built and calibrated using this workflow to match complex pressure transients measured in the field. We outline different geological scenarios that could potentially reproduce the pressure response observed in the field, highlighting the challenge of non-uniqueness when analysing well-test data. However, integrating other field data into the analysis allowed us to narrow the range of uncertainty, enabling the most plausible geological scenario to be taken forward for more detailed reservoir characterisation and history matching. The results provide new insights into the reservoir geology and the key flow processes that generate the pressure response observed in the field. This paper demonstrates that the geoengineering workflow used here can be applied to better characterise naturally fractured reservoirs. We also provide reference solutions for interpreting well-tests in fractured reservoirs where troughs in the pressure derivative are recognisable in the data.

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Macro Insights from Interval Pressure Transient Tests: Deriving Key Near-Wellbore Fracture Parameters in a Light Oil Reservoir Offshore Norway

Cited by 4 publications

References 15 publications

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Calibration of naturally fractured reservoir models using integrated well-test analysis – an illustration with field data from the Barents Sea

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