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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.

Mitigation of greenhouse gas emissions is becoming a significant factor in all industries. Cement manufacturing is one of the industries responsible for greenhouse gas emissions, specifically carbon dioxide emissions. Pozzolanic materials have long been used as cement additives due to the pozzolanic reaction that occurs when hydrated and the formation a cementitious material similar to that of cement. In this study, shale, which is a common component found in wellbore drill cuttings, was used in various sizes and quantities to determine the effect it had on the mechanical properties of wellbore cement. The unconfined compressive strength of the cement containing shale was compared to the cement without shale to observe the effect that both the quantity and particle size had on this property. SEM–EDS microscopy was also performed to understand any notable variations in the cement microstructure or composition. The samples containing micron shale appeared to have the best results of all the samples containing shale, and some of the samples had a higher UCS than one or more of the base case samples. Utilization of cuttings as a cement additive is not just beneficial in that it minimizes the need for cuttings removal and recycling, but also in that it reduces the amount of greenhouse gas emissions associated with cement manufacturing.

Porosity can be obtained from drilling data by using different correlations that relate the porosity to the unconfined compressive strength (UCS), which is obtained from drill bit inverted rate of penetration (ROP) models. Knowing the porosity at a given depth can benefit in helping to define the formations being penetrated and to characterize variations in a reservoir, thereby benefitting in selective stimulation. In this paper, previous studies that present methods for calculating porosity from UCS values will be compared and evaluated with sections of porosity that have been calculated from log data taken from three wells in Alberta, Canada. The correlations that will be compared include: Onyia, Sarda, Erfourth, and the UCS-gamma ray methods. The Onyia, Sarda, and Erfourth correlations are previously published while the UCS-gamma ray method correlates UCS in conjunction with the gamma ray at the bit. The porosity values that are found through these correlations are then plotted and their trends compared to each other as well as to the porosity obtained from log data in different sections from the well in Alberta, Canada. This process will help to determine what formation types are best correlated to the individual correlation. Typical drilling data is used in an inverted ROP model to obtain UCS. The UCS and gamma ray values are then taken and related to the porosity through the correlations presented in this paper and compared to the porosity determined from log data. Examining the different correlations that have been analyzed in various types of formations yield information indicating which correlation is best correlated to a specific formation type. The comparison's show that the predictability for some correlations are reasonable for limited datasets and sections of the well. To reasonably predict porosity values for mixed lithologies or shale formations, the integration of gamma log data is necessary. The trends exhibited from the correlations show that the comparison between porosity in shale is better seen when using the integrated UCS-gamma ray correlation. Utilizing the new UCSgamma ray model seemingly indicates that this useful new method can more accurately predict porosity variations in mixed lithologies and in shale reservoir sections. Bettering stimulation placement as well as minimizing logging in the reservoir can greatly reduce the overall cost of the operation. The improved selective stimulation process could also allow for higher production rates and/or potential reduced stimulation cost, thus increasing overall profit. This template is provided to give authors a basic shell for preparing your manuscript for submittal to an SPE meeting or event. Styles have been included to give you a basic idea of how your finalized paper will look before it is published by SPE. All manuscripts submitted to SPE will be extracted from this template and tagged into an XML format; SPE's standardized styles and fonts will be used when laying out the final manuscript. Links will be added to your manuscript for r...