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Cement fluid loss can cause loss of cement quality and its functionality. A common cement system with fluid loss problems is a system containing weighting agents. Barite, which is used in cement to increase the slurry weight, is one example. In this paper, the effect of barite nanoparticles (NPs) in different concentrations on cement fluid loss was investigated. Barite NPs were generated via dry grinding method using a high-energy ball grinder. The barite NPs were added to the cement slurry in various concentrations, and a low pressure low temperature (LPLT) static fluid loss tester was used to measure the cement fluid loss in the laboratory. It was observed that, as the nanoparticles concentration increases from 0 to 5 percent by weight of total slurry, the average fluid loss decreases by about half. Results showed that about 50 percent filtration reduction can be achieved by replacing 3% weight of normal barite with barite NPs in the cement slurry. The experimental tests presented herein also show the effect the fluid loss has on cement thickening time, clearly identifying that the cementing fluid loss must be integrated into the design of the cementing operation design and pumping schedule. It verified that adding barite NPs to cement slurry is promising from both a technical and cost saving perspective. The novelty of this research is in the application of barite NPs at low concentrations to reduce cement fluid loss significantly. Reducing the cement fluid loss can prevent many problems such as low quality cement due to poor crystallization, high equivalent circulation density (ECD), which results in formation failure during cementing jobs. The results presented herein also show a significant fluid loss effect on the cement thickening time. Lower cement thickening time seen due to fluid losses to the formation can cause the cement to set before completing cement placement, fluid channeling between the cement and formation, and low compressive strength cement.

Over the years, oil companies have continually looked for unique ways to advance technology in the drilling industry, with one of the significant achievements being horizontal drilling techniques. Horizontal wells allow for a more extended reach into the reservoir, resulting in more oil being extracted per well, however, these horizontal sections increase drilling distance and ultimately cost. These higher drilling costs increase the need for better optimization methods. Many researchers have analyzed theoretical ROP equations for optimization, though most have only incorporated constant drilling parameters in these equations. Since formation variables change with depth, so should the optimum drilling variables. Therefore, constant parameters waste both time and money, which could be immensely improved with the use of dynamic drilling parameters. The method presented herein, incorporates a Particle Swarm Optimization (PSO) algorithm to a rate of penetration (ROP) model in order to minimize the overall cost per foot of the well. This is achieved by allowing the PSO to find the best combination of drilling parameters, downhole weight on bit (DWOB) and revolutions per minute (RPM) of the drill bit, along with optimized pull depth and bit combinations. The application of this algorithm could be applied in a variety of ways including for operating oil and gas companies to plan for new wells or as an artificial intelligence component of a drilling simulator. A long term use could be as an autonomous driller constantly getting information updates and solving real time optimal solutions.

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.