Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference

Artun, Emre; Kulga, Burak

doi:10.1016/s1876-3804(20)60058-1

Cited by 22 publications

(10 citation statements)

References 21 publications

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“…Una vez hecha la fusificación, el motor de inferencia emplea las reglas si-entonces -definidas por los conocedores del sistema-para convertir las entradas difusas en salidas difusas (Artun & Kulga, 2020). Las reglas deben definir todas las combinaciones posibles de las variables para la toma de decisiones o, en este caso, para la valoración de la calidad.…”

Section: Figura 1 Sistema De Inferencia Difuso Mamdaniunclassified

Calidad del servicio de una empresa comercializadora, empleando un modelo SERVPERF difuso

Sandoval¹,

Pérez

Echeverría

et al. 2021

RECEIN

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El presente artículo mide la satisfacción de los clientes en una empresa comercializadora de productos para limpieza. El método de investigación empleó muestro intencional, delimitándose a su cartera de clientes. Se aplicaron 90 encuestas diseñadas a partir de la metodología SERVPERF. El análisis de los datos se realizó empleando lógica difusa para reducir la ambigüedad de las respuestas. En la medición de satisfacción del cliente se proponen ponderaciones para las dimensiones e ítems analizados, porque, aunque todas son relevantes, de acuerdo a los criterios operativos y estratégicos de las empresas, algunos pueden tener mayor relevancia para el cliente. La propuesta de la ponderación permitió crear un semáforo para categorizar a los clientes de acuerdo con su nivel de percepción. Se identificó que 4% de los clientes de la empresa está dispuesto a cambiar de proveedor, 30% consideran hacerlo, y el 66% pueden considerarse leales.

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Section: Figura 1 Sistema De Inferencia Difuso Mamdaniunclassified

Calidad del servicio de una empresa comercializadora, empleando un modelo SERVPERF difuso

Sandoval¹,

Pérez

Echeverría

et al. 2021

RECEIN

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“…In order to restore the productivity of a single well, refracturing is performed to reopen fractures that have failed or to open new ones. Refracturing, as a key technology to stimulate production in tight gas reservoirs, plays an important role in tight gas reservoirs [ 4 ]. Due to the long-term exploitation of tight gas and the continuous construction of gas wells in the gas field, the number of failed old wells has increased, and the physical properties of tight gas reservoirs have deteriorated and the stimulation effect has deteriorated [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Refracturing Effect of Tight Gas Reservoirs Based on Bayesian Inversion Algorithm

Lin

Zhou

Yong

et al. 2022

Computational Intelligence and Neuroscience

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As a key technology for tight gas stimulation, refracturing plays an important role in tight gas development. In the production process of tight gas wells, the reservoir or fracturing process may cause the hydraulic fractures to gradually fail and the production to continuously decrease. In order to restore the productivity of a single well, it is necessary to refract the well to reopen the failed fractures or fracturing. Reasonable refracturing timing and optimization of refract fracture parameters are important guarantees to ensure the benefits of refracturing in tight gas wells, and relevant research on it can provide theoretical and technical guidance for field construction design. Based on the inverse problem of the dynamic prediction model of tight gas well productivity, this paper proposes an inversion method of formation and fracture parameters before refracturing based on Bayesian inversion algorithm. Finally, based on the geology and development data of the fractured wells in the Sulige gas field, the field application of refracting well selection, determination of refracting reasonable timing, and prediction of refracting effect is carried out. The actual production data are compared, and it is shown that this method can provide theoretical guidance for high-efficiency production-increasing construction on-site.

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“…Al-Jamimi and Saleh [ 21 ] used an FL tool to optimize the catalysts, and the FL model was successful in predicting catalyst performance. Artun and Kulga [ 22 ] used the FL to select candidate wells for refracturing in tight gas sand reservoirs. Karacan [ 23 ] used the FL model using 24 data points to estimate the recovery factors of miscible CO 2 .…”

Section: Introductionmentioning

confidence: 99%

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

et al. 2021

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Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model’s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.

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Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference

Cited by 22 publications

References 21 publications

Calidad del servicio de una empresa comercializadora, empleando un modelo SERVPERF difuso

Calidad del servicio de una empresa comercializadora, empleando un modelo SERVPERF difuso

Prediction of Refracturing Effect of Tight Gas Reservoirs Based on Bayesian Inversion Algorithm

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

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