Real-time drilling mud gas monitoring for qualitative evaluation of hydrocarbon gas composition during deep sea drilling in the Nankai Trough Kumano Basin.

BACKGROUND: Integrated Ocean Drilling Program Expedition 338 was the second scientific expedition with D/V Chikyu during which riser drilling was conducted as part of the Nankai Trough Seismogenic Zone Experiment. Riser drilling enabled sampling and real-time monitoring of drilling mud gas with an onboard scientific drilling mud gas monitoring system ("SciGas"). A second, independent system was provided by Geoservices, a commercial mud logging service. Both systems allowed the determination of (non-) hydrocarbon gas, while the SciGas system also monitored the methane carbon isotope ratio (δ(13)CCH4). The hydrocarbon gas composition was predominated by methane (> 1%), while ethane and propane were up to two orders of magnitude lower. δ(13)CCH4 values suggested an onset of thermogenic gas not earlier than 1600 meter below seafloor. This study aims on evaluating the onboard data and subsequent geological interpretations by conducting shorebased analyses of drilling mud gas samples. RESULTS: During shipboard monitoring of drilling mud gas the SciGas and Geoservices systems recorded up to 8.64% and 16.4% methane, respectively. Ethane and propane concentrations reached up to 0.03 and 0.013%, respectively, in the SciGas system, but 0.09% and 0.23% in the Geoservices data. Shorebased analyses of discrete samples by gas chromatography showed a gas composition with ~0.01 to 1.04% methane, 2 - 18 ppmv ethane, and 2 - 4 ppmv propane. Quadruple mass spectrometry yielded similar results for methane (0.04 to 4.98%). With δD values between -171‰ and -164‰, the stable hydrogen isotopic composition of methane showed little downhole variability. CONCLUSIONS: Although the two independent mud gas monitoring systems and shorebased analysis of discrete gas sample yielded different absolute concentrations they all agree well with respect to downhole variations of hydrocarbon gases. The data point to predominantly biogenic methane sources but suggest some contribution from thermogenic sources at depth, probably due to mixing. In situ thermogenic gas production at depths shallower 2000 mbsf is unlikely based on in situ temperature estimations between 81°C and 85°C and a cumulative time-temperature index of 0.23. In conclusion, the onboard SciGas data acquisition helps to provide a preliminary, qualitative evaluation of the gas composition, the in situ temperature and the possibility of gas migration.

On-line multi-gas component measurement in the mud logging process based on Raman spectroscopy combined with a CNN-LSTM-AM hybrid model.

The qualitative and quantitative analysis of gas components extracted from drilling fluids during mud logging is essential for identifying drilling anomalies, reservoir characteristics, and hydrocarbon properties during oilfield recovery. Gas chromatography (GC) and gas mass spectrometers (GMS) are currently used for the online analysis of gases throughout the mud logging process. Nevertheless, these methods have limitations, including expensive equipment, high maintenance costs, and lengthy detection periods. Raman spectroscopy can be applied to the online quantification of gases at mud logging sites due to its in-situ analysis, high resolution, and rapid detection. However, laser power fluctuations, field vibrations, and the overlapping of characteristic peaks of different gases in the existing online detection system of Raman spectroscopy can affect the quantitative accuracy of the model. For these reasons, a gas Raman spectroscopy system with a high reliability, low detection limits, and increased sensitivity has been designed and applied to the online quantification of gases in the mud logging process. The near-concentric cavity structure is used to improve the signal acquisition module in the gas Raman spectroscopic system, thus enhancing the Raman spectral signal of the gases. One-dimensional convolutional neural networks (1D-CNN) combined with long- and short-term memory networks (LSTM) are applied to construct quantitative models based on the continuous acquisition of Raman spectra of gas mixtures. In addition, the attention mechanism is used to futher improve the quantitative model performance. The results indicated that our proposed method has the capability to continuously on-line detect 10 hydrocarbon and non-hydrocarbon gases in the mud logging process. The limitation of detection (LOD) for different gas components based on the proposed method are in the range of 0.0035%-0.0223%. Based on the proposed CNN-LSTM-AM model, the average detection errors of different gas components range from 0.899% to 3.521%, and their maximum detection errors range from 2.532% to 11.922%. These results demonstrate that our proposed method has a high accuracy, low deviation, and good stability and can be applied to the on-line gas analysis process in the mud logging field.