The role of AVO in prospect risk assessment

Roden, Rocky; Forrest, Mike; Holeywell, Roger; Carr, Matthew; Alexander, Polyakov

doi:10.1190/int-2013-0114.1

Cited by 20 publications

(4 citation statements)

References 37 publications

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“…It is therefore also important to adequately rate and integrate the qualities of the various geophysical data into the risking process (Forrest et al, 2010). The database, presently consisting of over 360 prospects, contains wells from a variety of AVO classes (Roden et al, 2014) and geographical locations. Classes 3, 2 constitute the bulk of prospects, with a few from class 1 and 4 settings.…”

Section: Hydrocarbon Risk Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Machine Learning for Hydrocarbon Prospect Risking

Mustafa¹,

AlRegib²,

Koster³

2023

Preprint

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Hydrocarbon prospect risking integrates information from multiple geophysical data and modalities to arrive at a probability of success for a given prospect. The DHI database of drilled prospects gathers data from prospects drilled around the world in multiple geologic settings in one central knowledge base. A major goal of interest to geophysicists is to understand the impact of various seismic amplitude anomalies, that are interpreted as direct hydrocarbon indicators, on the risking process. The individual correlation-based analysis typically carried out for this purpose misses out on complex feature interactions governing the physical phenomena. Data-driven machine learning techniques have the potential to sift through large, multidimensional datasets to learn mappings from feature spaces to outcome classes. LIME is a model explainability technique that explains decisions by black-box models by locally approximating their behavior. We propose a novel method whereby LIME is used in conjunction with various machine learning models to learn mappings from feature spaces in the DHI database to respective prospect outcomes. Consequently, we are able to highlight seismic amplitude anomalies considered most important by the models to the risking process and we show that our insights agree with geophysical intuition. Moreover, we use LIME explanations to demonstrate a case study of bias detection with machine learning models for the application of prospect risking. A limitation with LIME is that it only explains model behavior around solitary datapoints. Towards this end, we propose novel metrics summarizing a model's global understanding of a dataset by aggregating local explanations over individual examples. To the best of our knowledge, this is the first work using explainable machine learning for the purpose of bringing novel insights to prospect risk assessment.

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Section: Hydrocarbon Risk Assessmentmentioning

confidence: 99%

“…Amplitude downdip conformance (see Figure 2) and amplitude consistency in the mapped target area were identified to be important attributes across both classes. Similarly, Roden et al (2014) identified the most important AVO characteristics for prospect risk assessment.…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning for Hydrocarbon Prospect Risking

Mustafa¹,

AlRegib²,

Koster³

2023

Preprint

View full text Add to dashboard Cite

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“…By assigning different Vp, Vs, and densities according to different AVO classes, the response for various AVO classes can be regenerated by AVO forward modeling technique. More rigorous modeling could reveal the correct lithology (Roden et al 2014). According to Break et al 2006;Lasitha et al 2019 in the pre-stack domain, the offset or angle is indicative of three important aspects of seismology.…”

Section: Direct Hydrocarbon Indicator and Analysis Of Pre-stack Gathersmentioning

confidence: 99%

Post-drill geophysical characterization of two deep-water wells of Cauvery Basin, East Coast of India

Mishra¹,

Patidar

2022

J Petrol Explor Prod Technol

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The depth of the reservoir causes an increase in the degree of uncertainty in the prediction of reservoir quality. High frequency is suppressed with depth because Earth functions as a low-pass filter. The seismic amplitudes observed at various interfaces are influenced by spherical divergence, transmission losses, mode conversions, and inter-bed multiples. Seismic data have numerous essential components that must be thoroughly examined during hydrocarbon prospect identification and maturation, including post-critical reflections, events coherency (in near and far offsets), mode conversion, and interbed multiples. Seismic amplitudes are typically derived from 2D/3D seismic data and utilized directly or indirectly for reservoir interpretation and better prediction of subtle geological and geophysical information. To accurately depict subsurface geological features, stratigraphic architecture, and reservoir facies, it should be used in conjunction with the existing paleoenvironment data. When employed alone, the subsurface geophysical data may lead to erroneous interpretation of subsurface lithologies and inaccurate reservoir property predictions. Understanding these factors could help interpreters make better use of seismic data while maturing and de-risking the prospectivity. This study examines the post-drill geophysical characterization of two exploratory wells that were drilled in the deep-water area of the Cauvery Basin, East Coast of India. Analysis and correlation with a discovery well is done to understand the sediment depositional heterogeneity and corresponding seismic amplitude response, primarily for the cemented reservoir (dry well). To discover prospects and subsequently de-risk the existing prospect inventory, a dashboard checklist for in-depth study of seismic and well data has been developed. The top-down geophysical analytical approach that has been presented will aid in defining reservoir characteristics generally, estimating deliverability, and subsequently raising the geological probabilities and chance of success (COS) of any exploration project. The findings of this study allow critical analysis of seismic data to distinguish between softer/slower/possibly better reservoir rocks and hard/fast/tight rocks.

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“…Fluid discrimination plays an important role in seismic exploration and reservoir description. Qualitative interpretation [1][2][3][4][5][6] and direct quantitative estimation [7][8][9][10][11] for rock properties are two main methods to discriminate different pore-fluid types. However, the qualitative interpretation, such as the bright-spot, dim-spot, and flat-spot techniques, has difficulties in discriminating the geofluid in complicated lithologic reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Inversion for Geofluid Discrimination Based on Poroelasticity and AVO Inversion

Wang

Zhou

et al. 2019

Geofluids

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Geofluid discrimination plays an important role in reservoir characterization and prospect identification. Compared with other fluid indicators, the effective pore-fluid bulk modulus is more sensitive to the property of fluid contained in reservoirs. We combine the empirical relations with deterministic models to form a new kind of linearized relationship between the mixed fluid/rock term and the fluid modulus. On the one hand, the linearized relationship can decouple the fluid bulk modulus from the mixed fluid/rock term; on the other hand, the decoupled terms are more stable especially in low-porosity situations compared with previous approaches. In terms of the new linearized equation of the fluid modulus, we derive a novel linearized amplitude variation with offset (AVO) approximation to avoid the complicated nonlinear relationship between the fluid modulus and the reflectivity series. Convoluting this linearized AVO approximation with seismic wavelets, the forward modeling is constructed to combine the prestack seismic records with the fluid modulus. Meanwhile, we introduce the Bayesian inference with multivariable Cauchy prior to the fluid modulus inversion for a stable and high-resolution solution. Model examples demonstrate the accuracy of the proposed linearized AVO approximation compared with the exact Zoeppritz equation and Aki-Richards approximate equation. The synthetic and field data tests illustrate the accuracy and feasibility of the proposed fluid modulus inversion approach for geofluid discrimination.

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The role of AVO in prospect risk assessment

Cited by 20 publications

References 37 publications

Explainable Machine Learning for Hydrocarbon Prospect Risking

Explainable Machine Learning for Hydrocarbon Prospect Risking

Post-drill geophysical characterization of two deep-water wells of Cauvery Basin, East Coast of India

Inversion for Geofluid Discrimination Based on Poroelasticity and AVO Inversion

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