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Hydraulic fracturing is the stimulation process during which fractures are created by pumping mostly water and sand into the formations. Hydraulic fracturing is done on almost 90% of gas wells in the United States. Selectively determining the fracturing intervals along the borehole is one of the most critical factors for optimizing stimulation and maximizing the net present value (NPV) of the well. In this study, an empirical model was developed to predict the formation porosity using surface drilling data and gamma ray (GR) at the bit without needing log data. In this study, data from three wells were used to develop an empirical model for porosity prediction through the use of drilling data. To find the best model, a differential evolution algorithm (DE) was applied to the space of solutions. The DE algorithm is a metaheuristic method that works by having a population of solutions, and it iteratively try to improve the quality of answers by using a simple mathematical equation. The developed model uses the unconfined compressive strength (UCS) obtained from an inverted rate of penetration (ROP) model and gamma ray (GR) at the bit to estimate the formation porosity. Data from three offset wells in Alberta, Canada were evaluated to find a porosity estimation model. The DE algorithm was used to search the infinite space of solutions to find the best model. The models reliability and accuracy were studied by conducting a sensitivity analysis then comparing the results to offset well data. There is good agreement between the models estimated porosity and porosity from the well log data. This paper presents results from individual well sections that compare the neutron porosity from logs in the field to the calculated porosity obtained from the newly developed correlation. The results show accurate quantitative matching as well as trends. The model presented can be applied to horizontal wells where the porosity can be mapped in addition to the UCS value from the drilling data at no additional cost. Based on this formation mapping log, optimum fracturing interval locations can be selected by taking the UCS and porosity of a formation into account. The suggested approach can also be used to determine the porosity in real-time. The novelty of this model is in the ability to estimate porosity using typically collected drilling data potentially in real time. By applying this model, there is no need for well services such as well logging to find hydraulic fracturing points, which significantly reduces the cost and time associated with the well completion operation. |
