Proppant Hydraulic Fracturing in Low Permeability and Low Acid-Soluble Carbonate Reservoir: A Case History

Pujiastuti, Silvie; Wijayanti, Elisa; Nugroho, Hestu Sapto; Istono, Noviadi; Wijaya, Rio; Komalasari, Novi

doi:10.2118/130518-ms

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Solid particles may also be purposely introduced to enhance production through proppant hydraulic fracturing (Pujiastuti et al, 2010), or for natural gas dehydration using solid dessicants (e.g., alumina, silica gel, molecular sieves) (Manning and Thompson, 1991). When present in pipelines, these particles need to be transported by the carrier fluid to prevent the deposition and accumulation of solids, which can lead to partial blockage of the pipe.…”

Section: Introductionmentioning

confidence: 99%

Confidence in Critical Velocity Predictions for Solids Transport

Soepyan

Sarica

Subramani

et al. 2014

SPE Annual Technical Conference and Exhibition

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Sand is often produced with oil and gas even though sand control practices are used. To successfully transport solids in pipelines, the fluid velocity must exceed the critical velocity. Solids transport models are used to predict this velocity. However, for the same input field or laboratory condition, the models' critical velocity predictions may differ by orders of magnitude. Furthermore, none of the models provide information regarding the confidence in their velocity predictions. This paper introduces a systematic methodology, which selects the appropriate solids transport models for predicting the operational critical velocity envelope to ensure solids transport in the pipe to within a predetermined confidence level. Given a field or laboratory condition as input, the approach generates: (1) velocity predictions and uncertainty bounds of all models to within a predetermined confidence level, (2) recommendation of appropriate models for calculating the critical velocity at the input condition, and (3) impact of the uncertainty of input condition, experimental data, and model on the overall uncertainty bounds. The methodology uses data clustering, model evaluation, optimization, and uncertainty propagation approaches. Based on three Case Studies, the velocity envelopes suggested by the methodology, at the 90% confidence level, are consistent with experimental observations. For input laboratory conditions, which are used for validation purposes, the contributions of model, input condition, and experimental data uncertainties to the velocity prediction uncertainty were between 50 -90%, 3 -20%, and 6 -40%, respectively. If the parameters of the selected models were fine-tuned, for the same input conditions, the above uncertainties contribute about 40 -90%, 4 -15%, and 1 -50%, respectively, to the velocity prediction uncertainty. For an input field condition (the fourth Case Study), the above values become about 35 -70%, 7 -25%, and 25 -45%, respectively; and about 25 -30%, 30 -32%, and 40 -42%, respectively, if the parameters of the models are fine-tuned prior to uncertainty estimation. The results of these Case Studies suggest that for laboratory conditions, most of the uncertainty in a model's critical velocity prediction comes from the model, while for field conditions, the uncertainties of the model, input condition, and data may have nearly equal contributions.

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Section: Introductionmentioning

confidence: 99%

Confidence in Critical Velocity Predictions for Solids Transport

Soepyan

Sarica

Subramani

et al. 2014

SPE Annual Technical Conference and Exhibition

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Combined Proppant and Acid Stimulation Method in Ultra-Low-Permeability Dolomite Reservoirs: Laboratory Investigation

Wang,

Li,

Zhou

et al. 2024

Arab J Sci Eng

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Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservoir

Zoveidavianpoor

Samsuri

Shadizadeh

2012

SPE Oil and Gas India Conference and Exhibition

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With current technology, it is only possible to extract 20% to 25% of the original oil in place from Iranian carbonate reservoirs, 10% less than the world average. In addition, formation damage is a serious problem in those reservoirs, which mainly caused by asphaltene precipitation, sand production, and ineffective stimulation method. The majority of mature carbonate reservoirs in Iran have low permeability and high skin values. Therefore, such reservoirs are capable of producing at commercial rates only if they are hydraulically fractured. Acid fracturing is usually reported as a standard method for fracturing in carbonate reservoirs. Hydraulic Fracturing (HF) technology, which was originally applied to overcome near wellbore damage, is a proper replacement stimulation method. It is evident that to adopt this technology, considerable efforts have to be strenuous in candidate-well selection. As asserted in the literature, even though a common practice, candidate-well selection is not a straightforward process and up to now, there has not been a well-defined approach to address this process. The techniques applied in HF candidate-well selection could be divided into two methods; conventional and advanced approaches. Conventional methods are not easy to use for nonlinear processes, such as candidate-well selection that goes through a group of parameters having different attributes and features such as geological aspect, reservoir and fluid characteristics, production details, etc. and that’s because it is difficult to describe properly all their nonlinearities. However, it is believed that advanced methods such as Fuzzy Logic (FL) could be better decrease the uncertainty existed in candidate-well selection. This paper presents a Mamdani fuzzy model where rules for HF candidate-well selection were derived from multiple knowledge sources such as existing literature, intuition of expert opinion to verify the gathered information. The needs for adapting HF as replacement stimulation in Iranina carbonate reservoirs are discussed and advanced methods for HF candidate selection will be reviewed in this paper. Also, the main reasons which show why propped HF is the choice in carbonate reservoirs will be discussed. Finally, the proposed Fuzzy system model is applied along with a case study in a carbonate reservoir.

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Proppant Hydraulic Fracturing in Low Permeability and Low Acid-Soluble Carbonate Reservoir: A Case History

Cited by 5 publications

References 6 publications

Confidence in Critical Velocity Predictions for Solids Transport

Confidence in Critical Velocity Predictions for Solids Transport

Combined Proppant and Acid Stimulation Method in Ultra-Low-Permeability Dolomite Reservoirs: Laboratory Investigation

Development of a Fuzzy System Model for Candidate-well Selection for Hydraulic Fracturing in a Carbonate Reservoir

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