Seismic Characteristics of Mud Diapir in Song Hong Basin, Offshore Vietnam

Abstract

Seismic characteristics of mud diapir has been investigate over an area of 3900 km2, located in the central part of Song Hong basin, using four 2D seismic lines. There are six mud diapirs and three mud pipes have been documented. The core of the diapir is characterized as a zone of chaotic, disrupted seismic reflection, with the amplitude reflection ranging from low the high. High amplitude reflections are distributed in the top of the some diapir, which is possibly related to the gas accumulation. They are in different sizes, shapes, and the relationship with surrounding rock. They are characterized as deep sourced, high energy rooting from Oligocene/early Miocene shale layers. This organic rich shale rocks are in the oil and gas windows, thus their hydrocarbon generation combining with the tectonic inversion during Miocene make the overpressured shale and therefore rising the diaipirs. The area has experienced several phases of eruption in Middle Miocene, Late Miocene, Late Pliocene and Present day. The discovery and identification of the occurrence of mud diapirs implied a great potential for prediction of structural traps in the central part of the Red River Basin.

Application of artificial neural network and seismic attributes to predict the distribution of Late Oligocene sandstones in the Cuu Long basin

“…Generally, frequency attributes relate to bed thickness, wave scattering, and absorption. Time attributes relate to structure and amplitude attributes relate to stratigraphy (Le and Ha, 2020).…”

“…For example, Nguyen et al (2019) showed limited conditions of the number of drilled wells in the Phu Khanh basin, the application of ANN has been effective when integrating the results of analysis of seismic data, well-logs, core analysis to predict the distribution and quality of potential reservoir in the basin. In the Song Hong basin, Le and Ha (2020) used ANN to integrate 2D seismic attributes to identify mud diapirs and hydrocarbon traps. Tran et al (2020) used ANN to predict the distribution of volcanic materials in Cuu Long basin.…”