Research on predicting the growth trend of natural gas reserves and production will help provide a scientific basis for natural gas exploration and development. The metabolically improved modified weight coefficient GM(1,n) method is applied to the multi-cycle Hubbert model to predict the trend of new proven natural gas reserves in the Sichuan Basin. The ultimate recoverable reserves (URR) is introduced as a boundary condition in the production-time series to predict the natural gas production growth. The research results show that: (1) The annual newly added proven natural gas reserves of the Sichuan Basin maintain a multi-cycle growth trend, which will reach the peak reserves in 2034, at which time the proven rate of natural gas will reach 36%. (2) Based on the predicted results of proven reserves, the final recoverable reserves of natural gas are estimated to be $$5.25-5.75\times {10}^{12}{m}^{3}$$ 5.25 - 5.75 × 10 12 m 3 . The production in 2035 will reach $$750-810\times {10}^{8}{\mathrm{m}}^{3}/\mathrm{a}$$ 750 - 810 × 10 8 m 3 / a , and production will grow rapidly. The exploration and development of natural gas in the basin will be prospective for a long time.
Research on predicting the growth trend of natural gas reserves will help provide theoretical guidance for natural gas exploration in Sichuan Basin. The growth trend of natural gas reserves in Sichuan Basin is multi-cycle and complex. The multi-cyclic peak is screened by the original multi-cyclic peak judgment standard. Metabolically modified GM(1,3) gray prediction method is used to predict the multi-cycle model parameters. The multi-cycle Hubbert model and Gauss model are used to predict the growth trend of natural gas reserves. The research results show that: (1) The number of cycles of natural gas reserves curve during 1956–2018 is 13. Natural gas reserves will maintain the trend of rapid growth in the short term. (2) Metabolism modified GM(1,3) gray prediction model can improve the accuracy of model prediction. The prediction accuracy of Hubbert model is higher than that of Gauss model. By 2030, the cumulative proven level of natural gas will reach 52.34%. The Sichuan Basin will reach its peak of proven lifetime reserves in the next few years.
The shale gas exploration and development potential in the Sichuan Basin is huge. Production prediction and risk quantification are important in planning of natural gas resources. Hubbert and Gauss models are used to predict the growth trend of production in the gas reservoir. Based on the prediction results, the Monte Carlo simulation is used to calculate the probability of production realization. The evaluation matrix of risk level is established by using indices of production realization probability and dispersion degree for assessing the risk level of shale gas production. The results show that: 1) when URR is at the same growth rate, Gauss model has a more stable yield growth trend than Hubbert model, and the correlation coefficients of Gauss model are all higher than that of Hubbert model. This means that the production prediction results of the Gauss model have higher accuracy. 2) According to the Gauss model, the shale gas will reach peak production of (280–460) × 108 m3/a in 2042 and will have stable production from 2037 to 2047. By the end of the stable production stage, the URR exploitation degree is about 60%; 3) The Monte Carlo method can be used to obtain the production realization probability for each year. The risk level evaluation matrix can be established by taking the probability of realization and the dispersion degree as evaluation indices, which can provide the systematization of the risk levels. This study is helpful to deepen the understanding of natural gas exploration and development. And it is of great significance to gas field development planning and production index realization.
After a new round of tight gas geological evaluation was launched in 2018, a new chapter of tight gas exploration and development has been opened in the Sichuan Basin. In order to make better planning work, it is very important to study the variation rule and risk assessment of tight gas production. In this paper, the peak production is predicted by Ward model. Based on the prediction results, Hubbert and Gauss models were established to study the variation law of tight gas production, and the accuracy and prediction results of the models were determined by the degree of fitting and correlation coefficient. By studying the relationship between URR and production, it is concluded that the production increases in a step, and the future production of tight gas is simulated from the perspective of realization probability. Finally, the risk assessment matrix is established to study the difficulty degree of achieving the production target. The results are as follows: 1) Hubbert model has higher accuracy in predicting tight gas production change. The peak year of tight gas is 2042, the peak production is (86−106)×108m3/a, and the final URR recovery degree is about 60%. 2) The realization probability of production is calculated, and the possibility of reaching the target value is evaluated from the perspective of risk, so as to guide the production of tight gas, and finally promote the formulation of tight gas development planning in the Sichuan Basin.
Marine basin contains abundant natural gas resources in China, especiallythe Palaeozoic is becoming a hotspot of natural gas exploration, wherea series of large gas fields have been constantly discovered. It isbroad consensusthathigh-post mature source rocks (SRs)is gas-prone and thermal sulphatereduction (TSR) could accelerate the hydrocarbon cracking, which would be the dominant origin of gas enriched in the Palaeozoic of petroliferous Basin in China. In addition, these cracking gas is rich in H2S and CO2, due to the marine source rocksare rich inargillaceous sedimentary and sulfates. Nevertheless, the accumulations of gas reservoirs were obviously different between two giant marine craton in China. Sichuan basin is abundance of marine cracking gas, which was mainly generated around 257-205Ma. But it is rarely found marine oil reservoirs in Sichuan basin. In contrast, abundance of marine condensates are rich in Tarim Basin. Gas washing and migration fractionation effect would be the major formation mechanism of secondary condensates, which coexist with dry gas and normal oil reservoirs. These various reservoirs orderly distribute around source rocks in the Manjia’er sag of Tarim Basin. The origin of marine gases was mainly mixed from the hydrocarbon cracking derived via TSR and the different stages of thermal evolution of marine SRs, as well as portion of humic gas. Therefore, the carbon isotopic composition of diverse gas reservoirs is quite complex and the phenomenon of isotopic reversal δ13C1>δ13C2is pervasive. Eventually, two typical accumulation mechanismsof marine natural gas reservoirshave been established. It could be inferred the exploration of cracking gas is given priority in SichuanBasin and marine secondary condensatesshould be paid more attention during the petroleum exploration in Tarim Basin.
In the early stages of exploration, with only a limited amount of data available, it is difficult to evaluate a reservoir and optimize the sequence of the development plan. The score system is often used to rank the reservoir based on multidisciplinary factors that combine geology, production, and economics. However, current methods that are widely employed to classify the reservoir, such as analogy or single parameter, are qualitative or inaccurate, especially for carbonate gas reservoirs with complex geological conditions. In this study, we developed a score system using a data-driven approach to rank carbonate gas reservoirs in the Sichuan Basin. We developed two approaches, expert scoring and the random forest, to rank the quality of the reservoir, which agreed well with the field development plan. The expert scoring approach, which is highly dependent on the experience of experts in this area, is more suitable for reservoirs with limited data available, especially in the early exploration stage. The random forest model, which is more robust and able to reduce uncertainty from experience, is more suitable for developed areas with sufficient data. The developed score system can help rank new resource recovery and optimize the development plan in the Sichuan Basin.
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