Sand Production Potential Near Inclined Perforated Wellbores

Wang, Y.; Dusseault, Maurice B.

doi:10.2118/96-70

Cited by 7 publications

(1 citation statement)

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“…Some of these models assume sanding is due to seepage (tensile failure) while others base it on the strain level (compression). Most of the models predict sanding initiation 4,7,8,9,10,11,12,13 . Some other models view sand production as a mixed hydro-mechanical process 14,15,16,17,18 .…”

Section: Introductionmentioning

confidence: 99%

Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation

Nouri

Belhaj

Islam

2004

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractUsing novel physical model experiments and numerical analyses, a set of criteria are proposed that can be used to determine the onset of sanding and its severity in terms of rate and duration.Experiments on large-scale hollow cylinder specimens were conducted involving real time sand production measurement under various conditions. Synthetic sandstone was used in the experiments whose strength properties were comprehensively determined. A numerical approach was used for simulating the proposed experiments. The material behaviour was simulated using an elasto-plastic stress-strain relationship. The model simulates interaction between fluid flow and mechanical deformation of the medium in predicting sand production. The criteria considered for sanding are more comprehensive than conventionally used and included modeling strain softening of the material accompanied with shear bands formation as well as tensile failure. In the post disaggregation phase, additional features are considered including allowing for the removal of the disaggregated material and making the necessary adjustments to the size and properties of the domain under consideration. Hence, the model is considered to be suitable for time-dependent analysis of the rock as it undergoes disaggregation and production induced by depletion, drawdown, and water-cut. By relating the sand rate to production over the life of a field, the tool can be used in assisting with the completion and operation design of the wells.The model shows good agreement with experimental results in terms of rock deformation and sand rate. It predicted the onset of shear failure and the subsequent strain softening initiated from the cavity face and propagated inside the medium. Strain softening proved to be the main mechanism for material disaggregation. The good agreement between the numerical and experimental results under comprehensive and complex conditions bodes well for its field applications.

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Section: Introductionmentioning

confidence: 99%

Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation

Nouri

Belhaj

Islam

2004

SPE Annual Technical Conference and Exhibition

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Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan

Shabdirova,

Kozhagulova,

Samenov

et al. 2024

Nat Resour Res

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This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells.

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Stabilization of porous reservoir by stressed filter

Pyatakhin

2006

Journal of Petroleum Science and Engineering

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Sand Production Potential Near Inclined Perforated Wellbores

Cited by 7 publications

References 0 publications

Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation

Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation

Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan

Stabilization of porous reservoir by stressed filter

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