Integrating Qualitative and Quantitative Drilling Risk Prediction Methods for Shale Gas Field in Sichuan Basin
- GaoCheng Wang (PetroChina Zhejiang Oilfield Company) | Chunduan Zhao (Schlumberger) | Xing Liang (PetroChina Zhejiang Oilfield Company) | Yuanwei Pan (Schlumberger) | Lin Li (PetroChina Zhejiang Oilfield Company) | Lizhi Wang (Schlumberger) | Yun Rui (PetroChina Zhejiang Oilfield Company) | Qingshan Li (Schlumberger)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- 1.12.3 Mud logging / Surface Measurements, 5.8 Unconventional and Complex Reservoirs, 7.2 Risk Management and Decision-Making, 1.6 Drilling Operations, 7 Management and Information, 7.2.4 Statistical Techniques for Managing Risk, 1.1 Well Planning, 1.1 Well Planning, 5.1.5 Geologic Modeling, 0.2 Wellbore Design, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.8.2 Shale Gas, 1.12 Drilling Measurement, Data Acquisition and Automation, 5 Reservoir Desciption & Dynamics
- shale gas, natural fracture, Drilling risk, Ant tracking, Geomechanics model
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Huangjinba shale gas field is located at the south edge of the Sichuan Basin. It has very complex structures, in situ stresses and natural fracture corridors in comparison to adjacent areas in the Sichuan Basin. In recent drilling campaigns, drilling risks have caused some wells to fail in reaching their planned total depth, eventually failing to deliver cost-effective gas production. In order to mitigate drilling risks, e.g. mud loss, collapse, stuck, hang up, gas kick, effective drilling risk prediction is an urgent challenge to address. Integrating quantitative drilling risk prediction methods with qualitative methods could increase the prediction accuracy and avoid or mitigate the drilling risk during the well deployment stage.
In this project, multiple seismic attributes were used to predict natural fracture distributions which qualitatively indicated the locations where drilling risks were likely occur. Comprehensive geophysical characterization was performed to identify natural fracture zones and patterns, and their mechanisms were validated by analyzing regional geological and tectonic evolution.
Image log data was then integrated into the natural fracture distribution prediction from seismic to build a DFN (Discrete Fracture Network). This combination of the DFN predicted from seismic data plus quantitative image log information allowed improved accuracy in the prediction of drilling risks.
Following this, natural fracture stability was analyzed by building a 3D geomechanics model in order to predict drilling complex qualitatively. A full field 3D geomechanics model was built through integrating seismic, geological structure, log and core data. The 3D geomechanical model includes 3D anisotropic mechanical properties, 3D pore pressure, and the 3D in-situ stress field. Through leveraging measurements from an advanced sonic tool and core data, the anisotropy of the formation was captured at wellbores and propagated to 3D space guided by prestack seismic inversion data. 3D pore pressure prediction was conducted using seismic data and calibrated against pressure measurements, mud logging data, and flowback data. The discrete fracture network model, which represented multi-scale natural fracture systems, was integrated into the 3D geomechanical model during stress modeling to reflect the disturbance on the in-situ stress field by the presence of the natural fracture systems.
From these models, a drilling map which quantitatively indicated the depth where drilling risk such as mud loss, gas kick, etc. occurred was created along the well trajectory.
This paper presents the highlights and innovations in seismic multi-attributes analysis and full-field geomechanics modeling which integrate qualitative and quantitative methods for drilling risk prediction.
|File Size||5 MB||Number of Pages||25|
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