Integrated Risk Assessment Approach Helped in Successful Drilling in a Horizontal Well in Complex Geological Settings –A Case Study From Offshore South East Asia

Chatterjee, Avirup

doi:10.29118/ipa19.e.131

Cited by 1 publication

(1 citation statement)

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“…Jesus et al (2020) considered that some uncertainties, such as geological complexity, lithostratigraphy, fluid content, and seismic resolution, are common risks in exploration, and proposed a method that uses spectral decomposition, pre-stack inversion, and seismic facies classification to reduce exploration risk. Chatterjee and Ghosh (2019) believes that comprehensive risk assessment methods can help to successfully drill horizontal wells in complex geological environments. Through identification, evaluation, and management of various risks, several horizontal wells in an offshore complex geological environment in Southeast Asia have been planned to reduce drilling and geological uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Fractal Simulation Method for Simulating the Resource Abundance of Oil and Gas and Its Application

Guo

Ren

et al. 2022

Math Geosci

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In this study, a fractal simulation method for simulating resource abundance is proposed based on the evaluation results of the exploration risk and prediction technology for the spatial distribution of oil and gas resources at home and abroad. In addition, a key technical workflow for simulating resource abundance was developed. Furthermore, the model for predicting resource abundance has been modified, and the objective functions for conditional simulation have been improved. A series of prediction technologies for predicting the spatial distribution of oil and gas resources have been developed, and the difficulties in visualizing the exploration risks and predicting the spatial distribution of oil and gas resources have been solved. Prediction technologies have been applied to the Jurassic Sangonghe Formation in the hinterland of the Junggar Basin, and good results have been obtained. The results indicated that within the known area, taking the known abundance as the constraint condition, the coincidence rate of the simulated quantities of the original model and the improved model with the actual reserves reached 99.98% after the conditional simulation, indicating that the conditional simulation was effective. In addition, with the improved model, the predicted remaining resources are 0.7899$$\times 10^{8}$$ × 10 8 t, which is 65% of the discovered reserves, and the original model predicts that the remaining resources are 3.5033$$\,\times \,10^{8}$$ × 10 8 t, which is 2.89 times greater than the discovered reserves. Compared with the area in the middle stage of exploration, the results of the improved model are more consistent, and the results of the original model are obviously larger, indicating that the improved model has a good predictive effect for the unknown area. Finally, according to the risk probability and remaining resource distribution, the favorable areas for exploration were optimized as follows: the neighborhood of the triangular area formed by Well Lunan1, Well Shimo1, and Well Shi008, the area near Well Mo11, the area east of Well Mo5, the area west of Well Pen7, the area southwest of Well Shidong1, and the surroundings, as well as the area north of Well Fang2. The application results show that these prediction technologies are effective and can provide important reference and decision-making for resource evaluation and target optimization.

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Section: Introductionmentioning

confidence: 99%