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Multi-fractured horizontal well (MFHW) technology is a key technology for developing unconventional reservoirs, which can generate a complex fracture network called a stimulated reservoir volume (SRV). Currently, there are many relative analytical models to describe the fluid seepage law, which are not suitable for volcanic reservoirs as of yet. The reasons are as follows: (1) due to the development of natural fractures, multi-scaled flow (matrix, natural fractures, SRV) should be considered to characterize MFHW flow in volcanic reservoirs; (2) non-Darcy flow and stress sensitivity should be considered simultaneously for seepage in volcanic reservoirs. Thus, this paper presents a novel MFHW analysis model of volcanic reservoirs that uses a multi-scale dual-porosity medium model and complex flow mechanisms. Laplace transformation, the Duhamel principle, the perturbation method and Stehfest numerical inversion are employed to solve the model to obtain dynamic pressure response curves. The results show that the pressure response curve can be divided into eight stages. Sensitivity analysis shows that the parameters of hydraulic fractures mainly affect the early flow stage. The parameters of the SRV region mainly affect the middle flow stage. The parameters of unreconstructed regions, non-Darcy flow and stress sensitivity mainly affect the late flow stage. [ABSTRACT FROM AUTHOR]

In the past, reservoir engineers used numerical simulation or reservoir engineering methods to predict oil production, and the accuracy of prediction depended more on the engineers' own experience. With the development of data science, a new trend has arisen to use deep learning to predict oil production from the perspective of data. In this study, a hybrid forecasting model (CNN-LSTM) based on a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) neural network is proposed and used to predict the production of fractured horizontal wells in volcanic reservoirs. The model solves the limitation of traditional methods that rely on personal experience. First, the production constraints and production data are used to form a feature space, and the abstract semantics of the feature time series are extracted through convolutional neural network, then the LSTM neural network is used to predict the time series. The certain hyperparameters of the whole model are optimized by Particle Swarm Optimization algorithm (PSO). In order to estimate the model, some production dynamics from the Xinjiang oilfield of China are used for comparative analysis. The experimental results show that the CNN-LSTM model is superior to traditional neural networks and conventional decline curves. [ABSTRACT FROM AUTHOR]

Production profiling technology is an important method for monitoring the dynamics of oil and gas reservoirs which can effectively improve the efficiency of oil recovery. Production profiling is a technique in which a test instrument is lowered from the tubing to the bottom of the well to measure flow, temperature, pressure, and density in a multi-layer section of a producing well. Normal production profiling process needs to stop production, operate complex, consume long time and high cost. Furthermore, the profile cannot be continuously monitored for a long time. To address these limitations, this paper proposes a production profiling interpretation method based on reservoir primitive microbial DNA sequencing. The microbial stratigraphic baseline with high-resolution features is obtained by sampling and DNA sequencing of produced fluid and cuttings from different wells. Specifically, the random forest algorithm is preferred and improved by comparing the accuracy, precision, recall, F1-score, and running time of three clustering methods: Naïve-Bayes classifier, random forest classifier, and back-propagation classifier. Constructing PSO-random forest model is based on stratigraphic records and produced fluid bacteria features. The computational accuracy and efficiency of this method allows it to describe the production profile for each formation. Moreover, this test process does not need to stop production with simple operation and does not pollute the formation. Meanwhile, by sampling fluid production at different stages, it can achieve the purpose of long-term effective dynamic monitoring of the reservoir. [ABSTRACT FROM AUTHOR]

The Yinchuan Graben is an important potential exploration area that is located on the western margin of the Ordos Basin. Over 8000 m of Cenozoic strata have been formed since the Cretaceous. With an integrated approach of cores observation, logging analysis, and geochemical analysis, we analyzed the characteristics of the Cenozoic source rocks in the Yinchuan Graben and determined the formation and destruction of the fossil oil reservoirs. With type III kerogen, the TOC of the dark mudstone in the Qingshuiying Formation is up to 7.5%, and the Ro is 0.95–1.04%, indicating the source rocks have entered the mature stage but the hydrocarbon generation potential is insufficient. A quantity of reservoir bitumen and oil-bearing fluid inclusions (GOI = 1.67–4%) were found in the Qingshuiying Formation sandstone in Well YQ-1, which indicates a fossil oil reservoir had existed. The fossil oil reservoir and reservoir bitumen were generated by the unexplored pre-Cenozoic strata in the Yinchuan Graben. The reservoir bitumen has high maturity and is associated with many fluid inclusions with a high homogenization temperature or CO2. This indicates that the bitumen was formed by the pyrolysis of the oil which was caused by the hot fluid migrating along with the deep fault belts. [ABSTRACT FROM AUTHOR]