Inversion of marine in-situ stress of northeast Sichuan and its influence on horizontal well completion optimization

2010 ◽  
pp. 327-332
Author(s):  
Kai Lan ◽  
Mingguo Liu ◽  
Youming Xiong ◽  
Kuangxiao Liu
2021 ◽  
Author(s):  
Danial Zeinabady ◽  
Behnam Zanganeh ◽  
Sadeq Shahamat ◽  
Christopher R. Clarkson

Abstract The DFIT flowback analysis (DFIT-FBA) method, recently developed by the authors, is a new approach for obtaining minimum in-situ stress, reservoir pressure, and well productivity index estimates in a fraction of the time required by conventional DFITs. The goal of this study is to demonstrate the application of DFIT-FBA to hydraulic fracturing design and reservoir characterization by performing tests at multiple points along a horizontal well completed in an unconventional reservoir. Furthermore, new corrections are introduced to the DFIT-FBA method to account for perforation friction, tortuosity, and wellbore unloading during the flowback stage of the test. The time and cost efficiency associated with the DFIT-FBA method provides an opportunity to conduct multiple field tests without delaying the completion program. Several trials of the new method were performed for this study. These trials demonstrate application of the DFIT-FBA for testing multiple points along the lateral of a horizontal well (toe stage and additional clusters). The operational procedure for each DFIT-FBA test consists of two steps: 1) injection to initiate and propagate a mini hydraulic fracture and 2) flowback of the injected fluid on surface using a variable choke setting on the wellhead. Rate transient analysis methods are then applied to the flowback data to identify flow regimes and estimate closure and reservoir pressure. Flowing material balance analysis is used to estimate the well productivity index for studied reservoir intervals. Minimum in-situ stress, pore pressure and well productivity index estimates were successfully obtained for all the field trials and validated by comparison against a conventional DFIT. The new corrections for friction and wellbore unloading improved the accuracy of the closure and reservoir pressures by 4%. Furthermore, the results of flowing material balance analysis show that wellbore unloading might cause significant over-estimation of the well productivity index. Considerable variation in well productivity index was observed from the toe stage to the heel stage (along the lateral) for the studied well. This variation has significant implications for hydraulic fracture design optimization, particularly treatment pressures and volumes.


2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.


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