An Empirical Model to Estimate a Critical Stimulation Design Parameter Using Drilling Data

Atashnezhad, A.; Cedola, A. E.; Hareland, Geir

doi:10.2118/185741-ms

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A. Atashnezhad et al presented analytical formulas for the front region of a single cutter based on the interfacial friction between the cutter face and rock. Validation of the model was carried out using different bit diameters, backward inclinations, drilling parameters and rock strengths [4,5] . Reza Rahmani et al evaluated the robustness of round and v-shaped PDC cutters against mechanical and thermal loads [6] .…”

Section: Introductionmentioning

confidence: 99%

Prediction of formation-PDC bit abrasiveness based on the fractal dimension of a rock surface

Wang,

Li,

et al. 2024

Preprint

View full text Add to dashboard Cite

During rock drilling, the drill bit will wear as it breaks the rock. However, there is no uniform grading standard for rock abrasiveness. To solve this problem, the wear mechanism of a PDC bit and a formation is analyzed in depth, and an abrasiveness evaluation method based on the fractal dimension of the rock surface topography is established. Initially, a three-dimensional digital model is generated from a SEM image post-drilling, followed by an evaluation of the rock surface's irregularities using an adapted W-M function to ascertain its fractal dimensionality. Then, the microcontact characteristics of the contact surface between the formation and the PDC bit are analyzed, and the distribution of the microconvex contact points of the two-body friction pair in a region is obtained. Because the sliding friction between the drill bit and the rock produces a large amount of heat, according to the contact area formula of the friction surface and heat conduction theory, the temperature rise process and temperature distribution of the formation drill bit under the condition of sliding friction are revealed, and the real contact area between the formation and the drill bit within a certain temperature range is obtained. Finally, the evaluation index of rock abrasiveness under sliding conditions is established by using the wear weight loss of the rock cutting tool per unit volume as the index of rock abrasiveness, and the model is verified by a microdrilling experiment. The research in this paper is highly important for improving the rock breaking efficiency and bit service life during drilling.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of formation-PDC bit abrasiveness based on the fractal dimension of a rock surface

Wang,

Li,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Various approaches to solving optimal wells spacing issue and optimization are found in the literature. In particular, genetic algorithms are used (Yeten et al, 2003;Güyagüler and Horne, 2004;Emerick et al, 2009;Lyons and Nasrabadi, 2013;Humphries and Haynes, 2015;Sampaio et al, 2015a;2015b;), particle swarm optimization (Onwunalu and Durlofsky, 2010;Qihong et al, 2012;Nwankwor et al, 2013;Isebor et al, 2014), differential evolution (Awotunde, 2014;Atashnezhad et al, 2017), harmony search algorithm Afshari et al, 2011), the "imperialist competitive" algorithm (Naderi and Khamehchi, 2017), the bat-inspired algorithm (Keshavarz and Nader, 2016;Naderi, 2017;Łętkowski, 2018), covariance matrix adaption evolutionary strategy (Bouzarkouna et al, 2012;Feng et al, 2016), analytic and semi-analytical methods (Hazlett and Babu, 2005), machine learning (Nwachukwu et al, 2018), generalized pattern search (Humphries, 2014), directional search (Aliyev, 2011).…”

Section: Introductionmentioning

confidence: 99%

Optimization algorithm for number and wells placement

Łętkowski¹,

Badawczy²

2019

View full text Add to dashboard Cite

Determination of the optimal number and placement of production wells is crucial for the effective depletion of the hydrocarbon reservoir. Due to the strongly non-linearity of the problem and the occurrence of multiple local minimums in the response function the non-gradient optimization methods in combination with reservoir simulations are most commonly used for its solution. However, it should be noted that most of the research works dedicated to this issue describe the process of placement optimization but not the number of drilling wells assuming that it was arbitrary set. This is partly due to the fact that known and used optimization methods operate on a fixed number of optimization parameters, therefore the number of production wells can not change during the optimization process. The paper is dedicated to the attempt to build an algorithm that allows simultaneous optimization of the number and position of production wells with respect to the discounted profit in a given period of operation. The basic optimization method in the presented algorithm is the Particle Swarm Optimization (PSO)-one of the most effective non-gradient optimization methods that belongs to the group of methods applying the swarm's intelligence. Taking into account the number of drilling wells in the optimization process means that the algorithm operates on a variable number of parameters. The objective algorithm starts optimization from an arbitrarily set number of producers, reducing it gradually. Efficiency tests conducted on the sample reservoir PUNQ-S3 indicated a satisfactory convergence of the proposed method. The computing program created implements the mechanisms of convergence enhancement by improving the boundary conditions for the optimization method. The minimum separation distance control between production wells was also introduced at the initial stage of optimization process. Although the algorithm is characterized by satisfactory convergence it would be advisable to improve it by using a hybrid method to increase its effectiveness in the local optimization phase and to introduce minimum well spacing during the entire optimization process.

show abstract

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 1: Reservoir Characterization

et al. 2021

View full text Add to dashboard Cite

Completion design for horizontal wells is typically performed using a geometric approach where the fracturing stages are evenly distributed along the lateral length of the well. However, this approach ignores the intrinsic vertical and horizontal heterogeneity of unconventional reservoirs, resulting in uneven production from hydraulic fracturing stages. An alternative approach is to selectively complete intervals with similar and superior reservoir quality (RQ) and completion quality (CQ), potentially leading to improved development efficiency. In the current study, along-well reservoir characterization is performed using data from a horizontal well completed in the Montney Formation in western Canada. Log-derived petrophysical and geomechanical properties, and laboratory analyses performed on drill cuttings, are integrated for the purpose of evaluating RQ and CQ variability along the well. For RQ, cutoffs were applied to the porosity (>4%), permeability (>0.0018 mD), and water saturation (<20%), whereas, for CQ, cutoffs were applied to rock strength (<160 Mpa), Young’s Modulus (60–65 GPa), and Poisson’s ratio (<0.26). Based on the observed heterogeneity in reservoir properties, the lateral length of the well can be subdivided into nine segments. Superior RQ and CQ intervals were found to be associated with predominantly (massive) porous siltstone facies; these intervals are regarded as the primary targets for stimulation. In contrast, relatively inferior RQ and CQ intervals were found to be associated with either dolomite-cemented facies or laminated siltstones. The methods developed and used in this study could be beneficial to Montney operators who aim to better predict and target sweet spots along horizontal wells; the approach could also be used in other unconventional plays.

show abstract

An Empirical Model to Estimate a Critical Stimulation Design Parameter Using Drilling Data

Cited by 3 publications

References 7 publications

Prediction of formation-PDC bit abrasiveness based on the fractal dimension of a rock surface

Prediction of formation-PDC bit abrasiveness based on the fractal dimension of a rock surface

Optimization algorithm for number and wells placement

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 1: Reservoir Characterization

Contact Info

Product

Resources

About