Transition of ductile and brittle fracture during DWTT by FEM

Globally, steel pipelines are widely used to transport energy in the form of liquid petroleum and natural gas. The steel used in the manufacture of these pipelines must have high strength and toughness, and high resistance to fracture. The Drop Weight Tear Test (DWTT) is the most widely used test to assess brittle fracture characteristics in steel. The zones of ductile and brittle fracture during DWTT characterize the quality of pipeline steels. In this paper, the Gurson-Tvergaard-Needleman (GTN) fracture models are coupled in a Finite Element model. The ductile and brittle fracture zones in the samples are analyzed under different conditions. The results show that the change in fracture mode during the DWTT is from the brittle to the ductile, then again to the brittle. The calculated absorbed energies during DWTT compare well with experimental findings. Finally, we present an analysis of the transition from ductile to brittle fracture under different conditions

Using a Deep Neural Network with Small Datasets to Predict the Initial Production of Tight Oil Horizontal Wells

Due to its abundant reserves, tight oil has emerged as a significant substitute for conventional petroleum resources. It has become one of the focal points of exploration and research, and a new hot spot in global unconventional oil and gas exploration and development. This has led to a significant increase in the demand for forecasting the production capacity of tight oil horizontal wells. The deep neural network (DNN), as a mature model, has demonstrated significant advantages in many fields. However, due to the confidentiality and uniqueness of oilfield data, acquiring large datasets has become a challenge. Traditional methods using small datasets for training DNN models result in low accuracy and overfitting issues, which hinders the development of neural networks in the petroleum industry. This study aims to predict the initial production capacity of tight oil horizontal wells by using a small dataset of 650 data points through a DNN model. The research results indicate that pre-trained and fine-tuned DNNs outperform shallow neural networks, supporting vector machines, and DNN trained with traditional methods in terms of better generalization performance. Their accuracy reached 91.3%, demonstrating that it is reasonable to use a small dataset with pre-trained and fine-tuned DNN models

Productivity Evaluation of Vertical Wells Incorporating Fracture Closure and Reservoir Pressure Drop in Fractured Reservoirs

In most oilfields, many wells produce in pseudo-steady-state period for a long time. Because of large reservoir pressure drop in this period, fractured reservoirs always show strong stress sensitivity and fracture closure is likely to occur near wellbores. The primary goal of this study is to evaluate productivity of vertical wells incorporating fracture closure and reservoir pressure drop. Firstly, a new composite model was developed to deal with stress sensitivity and fracture closure existed in fractured reservoirs. Secondly, considering reservoir saturation condition, new pseudo-steady productivity equations for vertical wells were derived by using the proposed composite system. Thirdly, related inflow performance characteristics and influence of some factors on them were also discussed in detail. Results show that fracture closure has a great effect on vertical well inflow performance and fracture closure radius is negatively correlated with well productivity. In this composite model, the effects of stress sensitivity of the inner and outer zone on well productivity are rather different. The inner zone’s stress sensitivity affects well productivity significantly, but the outer zone’s stress sensitivity just has a weak effect on the productivity. Strong stress sensitivity in the inner zone leads to low well productivity, and both inflow performance and productivity index curves bend closer to the bottom-hole pressure axis with stress sensitivity intensifying. Meanwhile, both maximum productivity and optimal bottom-hole pressure can be achieved from inflow performance curves. In addition, reservoir pressure is positively correlated with vertical well productivity. These new productivity equations and inflow performance curves can directly provide quantitative reference for optimizing production system in fractured reservoirs

Evaluation of gas well productivity in low permeability gas reservoirs based on a modified back-pressure test method

In view of the long pressure stabilization time of low permeability gas reservoirs, the traditional backpressure test was modified based on the idea of isochronal test in order to evaluate gas well productivity accurately. Firstly, carry out continuous well startup using 3–4 incremental working systems at the same time interval without the bottom-hole flowing pressure reaching stability; then carry out a prolonged test using a reasonable working system which requires both the bottom-hole flowing pressure and the production reaching stability; finally shut in the well to allow the pressure recover to formation pressure. If the isochronal test productivity calculation method is borrowed for the modified backpressure test, the drawdown pressure will be overestimated, and calculated productivity will be underestimated. The “process conversion-flowing pressure correction” was used to convert the test process into an isochronal test process, and the bottom-hole flowing pressure correction equation was deduced based on pressure superposition principle to solve the productivity calculation problem with this method. The example indicates that the modified backpressure test method can not only shorten the test time significantly and avoid frequent well startup and shut-in, but also can ensure the accuracy of productivity calculation. Key words: low permeability gas reservoir, bottom-hole flowing pressure, modified backpressure test, “process conversion-flowing pressure correction”, productivity evaluatio

Water producing mechanisms of carbonate reservoirs gas wells: A case study of the Right Bank Field of Amu Darya, Turkmenistan

The mechanisms of carbonate gas reservoirs were systematically studied with the Right Bank Field of Amu Darya Gas Field, Turkmenistan, as an example. Water produced from the reservoirs has three sources, condensate water, engineering fluids and formation water. The fluid physical property and water-gas ratio (WGR) method for the single component conditions and the chloridion conservation method for the multi-components conditions were established to identify the components contained in the production fluids. A water production diagnosing curve, which refers to the degree of reserve recovery as a function of the water-gas ratio in the log-log coordinate curve, was then established and the formation water producing wells were divided into three patterns, i.e. Type 1, Type 2, and Type 3. Through in-depth studies of the static and dynamic reservoir characteristics of each pattern, the following understandings were attained: The reservoirs of Type 1 are mainly porous, and the water producing mechanism is bottom water coning along matrix pores; the reservoirs of Type 2 are mainly fractured-porous, and the bottom water produces basically through the natural fracture system; the reservoirs of Type 3 are mainly fractured-cavity, and the bottom water produces basically through large-scale fractures and caves. Key words: Turkmenistan, Amu Darya Basin, carbonate gas reservoir, water production source, diagnosing curve, water producing mechanis