Sour gas reservoirs with H 2 S and/or CO 2 are widely spread in the whole world, especially in France, Canada, America and China. Most of sour gas reservoirs in China are HPHT tight gas reservoirs which require long testing duration to reach steady state when conducting deliverability test while long testing time leads to huge operation risk because testing tools are used close to critical temperature and pressure condition.In order to resolve the contradiction among testing duration, operation risk and interpretation results' accuracy, a new deliverability test method is proposed by using pressure buildup transient data. The deliverability calculation model is built by integrating wellbore fluid flow and formation seepage considering fluid flow from formation to wellbore after well shut-in. The formation parameters are interpreted by one pressure buildup transient data, based on which, four daily production rates are designed to simulate the wellbore flow pressure until the well reaches steady state flow, and then the deliverability equation can be derived from the pressure & daily rate points under steady state flow.Filed case comparison is made between interpretation of actual test data and data from the new method. Results show that the new deliverability test method can get more accurate deliverability equation and AOF by only 1 time pressure buildup and greatly reduces the duration for testing, so it is very practicable because it can greatly decrease the operation risk and increase interpretation accuracy for HPHT sour gas reserviors.This new deliverability test method has wide application prospect in HPHT sour gas reservoirs and can be extended to other oil/gas reservoirs' application for quick AOF determination, rate allocation, and economic evaluation.
The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.
Wall roughness significantly influences both laminar-turbulent transition process and fully developed turbulence. A wall roughness extension for the KDO turbulence/transition model is developed. The roughness effect is introduced via the modification of the k and νt boundary conditions. The wall is considered to be lifted to a higher position. The difference between the original position and the higher position, named as equivalent roughness height, is linked to the actual roughness height. The ratio between the two heights is determined by reasoning. With such a roughness extension, the predictions of the KDO RANS model agree well with the measurements of turbulent boundary layer with a sand grain surface, while the KDO transition model yields accurate cross-flow transition predictions of flow past a 6:1 spheroid.
Wall roughness significantly influences both laminar-turbulent transition process and fully developed turbulence. This work has developed a wall roughness extension for the KDO turbulence/transition model. The roughness effect is introduced via the modification of the k and νt boundary conditions, i.e., the wall is considered to be raised at an extra height. The equivalent roughness height is linked to the actual roughness height, and the ratio between them is determined by reasoning. With such a roughness extension, the predictions of the KDO RANS model agree well with the measurements of turbulent boundary layer with a sand grain surface, while the KDO transition model yields accurate cross-flow transition predictions of flow past a 6:1 spheroid.
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