Using pressure transient testing is very useful to identify reservoir properties, such as permeability, and reservoir size. However, it can also be used as an evaluation tool to test the success of the fracturing process as well as identifying the fracture properties such as conductivity and size. This paper presents an investigation of the fracture properties and their effect on pressure and pressure derivative signature in multilateral wells at dry gas reservoir as well as the effect of the number of laterals, length of laterals and reservoir anisotropy.
The base case scenario was built using ECLIPSE simulator. More than 20 scenarios were used to generate the data and use the early production region as the drawdown feed data. different scenarios were investigated to analyze the pressure derivative signature and hence study how hydraulic fracture stages will appear in the curve and how can we utilize that to characterize our fracture. The effect of the lateral length and the number of laterals were studied then the fracture effect introduced to the base case starting from 1 stage fracturing up to 8 stage fracturing. Furthermore, the reservoir anisotropy and the fracture conductivity was explored.
The results of this study can be used as qualitative method to judge the success of the fracturing job by comparing the response before and after the fracturing. It was noticed that increasing the number of lateral and the length of lateral shifted the pressure derivative curve down. However, increasing the fracture stages while keeping the number of lateral constant affected only the early region.
Well testing techniques can be extended to study the complex geometry of multilateral well especially when hydraulic fractures are induced. It will be a great benefit to the industry to test their work with a quick method without the need to use downhole monitors.
Recently the multilateral drilling and hydraulic fracturing has gained a considerable momentum, because they maximize the exposure area and hence increase the hydrocarbon production. Many researchers have investigated the performance of single and multilateral wells considering the reservoir anisotropy (smith et al. 1995; Retnanto et al. 1996; Clifford et al. 1996; Yildiz and Ozkan 1997)
The horizontal wells are applied to enhance the production rate by increasing the contact area between the wellbore and reservoir, it has been also used to access the highly heterogeneous and unconventional formations. One horizontal well can produce the same amount of 5 vertical wells with a very competitive cost and operational time. Further improvement for the productivity of horizontal well can be achieved by conducting hydraulic fracture operations, especially for low permeable or unconventional formations. This paper shows a new technique to estimate the performance of hydraulically fractured horizontal wells, without a need for using downhole valves or smart completion.
In the literature, few empirical models have been proposed to evaluate the inflow performance of such wells. However, most of these models assume constant pressure drop in the horizontal section, therefore, significant errors were reported from those models. In this work, a reliable model will be presented to predict the well deliverability for hydraulically fractured horizontal well producing from heterogeneous and anisotropic formation. Different artificial intelligence (AI) methods were investigated to evaluate the well performance using a wide range of reservoir/wellbore conditions. The significant of several parameters on the well productivity were investigated including; permeability ratio (kh/kv), number of fracture stages and the length of horizontal section.
The AI model was developed and validated using more than 300 data sets. Artificial neural network (ANN) model is built to determine the production rate with an acceptable error of 8.4%. The model requires the wellbore configurations and reservoir parameters to quantify the flow rate. No numerical approaches or downhole well completions were involved in this ANN model, which reduce the running time by avoiding such complexity. Moreover, a mathematical relation was extracted from the optimized artificial neural network model. In conclusion, this work would afford an effective tool to determine the performance of complex wells, and reduce the differences between the actual production data and the outputs of commercial well performance software.