The figure below is from a siliciclastic reservoir mapping case study showing a High-Def gamma ray random line result between two wells. Well A was used to build the machine learning model. Well B was only used to build the compaction trend. The base of the reservoir sand is also indicated. A 120 Hz cap has been applied to the measured logs. It must be noted that previous attempts to map this sand using conventional seismic inversion proved to be unsuccessful. The data are provided by the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL).
High-Def Gamma Ray Case Study I
High-Def 3D gamma ray random line. A 120 Hz cap has been applied to the measured gamma ray logs.
The next figure compares the High-Def gamma ray with fullstack seismic, which does not have enough frequency content to adequately map the sand formation. Notice the increased level of layer detail in the gamma ray result throughout the analysis window, which is validated by the match with the measured logs.
Comparison of conventional seismic (top) and High-Def gamma ray. The reservoir sand is indicated by a white arrow. Well A was used to build the machine learning model. Well B is a blind well. The measured logs have a 120 Hz filter applied.
High-Def Gamma Ray Case Study II
The Jeanerette Field has been producing hydrocarbons since the 1930’s from the Planulina sands at a depth of 10-15K feet below the surface. The field is positioned on the flank of an intermediate piercement salt dome and consists of a sequence of deltaic channel/bar sands interbedded with deep-water shales deposited along the paleo-shelf. Exploration and production in this area is considered high risk and expensive due to complex structures, erratic sands and high pressures. In 2004, a well was drilled that penetrated two reservoirs. The lower produced 3.25 BCFG and 49 MBO. The operator shut production after eighteen months to re-complete in the upper reservoir sand, which has a thickness of 40 feet. During the recompletion, drilling tools were lost in the hole and subsequent failed fishing operations resulted in a catastrophic collapse of the casing. A second well was drilled nearby to reach the reservoir up-dip, but the result was a dry hole. In our study, we used prestack 3D seismic and the available well information to map the reservoir sand and determine why the second well missed the target.
Due to the low frequency content of the seismic and the thickness of the reservoir, seismic conditioning and high-definition rock property inversion using machine learning were carried out. The resulting gamma ray volume shows a clear compartmentalization within the reservoir, a possible explanation for the failure of the second well. The increased resolution not only enabled mapping of the lateral discontinuity of the sand, but also introduced enhanced fault expressions and stratigraphic details that were not visible using the conventional seismic information.
Random line comparison of conventional seismic (top) and High-Def gamma ray. The reservoir is indicated in red. Potential reservoirs are indicated in white. The gamma ray well log has a 140 Hz filter applied. The second well drilled was a dry hole and has no log data in the zone of interest. Based on this comparison, the second well may have just missed the target, which was drilled based on high amplitude up dip from the initial well.
