Using Particle Swarm Optimization to Compute Hundreds of Possible Directional Paths to Get Back/Stay in the Drilling Window

Fracture-driven interaction FDI (colloquially called “Frac-hit”) is the interference of fractures between two or more wells. This interference can have a significant impact on well production, depending on the unconventional play of interest (which can be positive or negative). In this work, the surrogate model was used along with metaheuristic optimization algorithms to optimize the completion design for a case study in the Bakken. A numerical model was built in a physics-based simulator that combines hydraulic fracturing, geomechanics, and reservoir numerical modeling as a continuous simulation. The stress was estimated using the anisotropic extended Eaton method. The fractures were calibrated using Microseismic Depletion Delineation (MDD) and microseismic events. The reservoir model was calibrated to 10 years of production data and bottom hole pressure by adjusting relative permeability curves. The stress changes due to depletion were calibrated using recorded pressure data from MDD and FDI. Once the model was calibrated, sensitivity analysis was run on the injected volumes, the number of clusters, the spacing between clusters, and the spacing between wells using Sobol and Latin Hypercube sampling. The results were used to build a surrogate model using an artificial neural network. The coefficient of correlation was in the order of 0.96 for both training and testing. The surrogate model was used to construct a net present value model for the whole system, which was then optimized using the Grey Wolf algorithm and the Particle Swarm Optimization algorithm, and the optimum design was reported. The optimum design is a combination of wider well spacing (1320 ft), tighter cluster spacing (22 ft), high injection volume (1950 STB/cluster), and a low cluster number per stage (seven clusters). This study suggests an optimum design for a horizontal well in the Bakken drilled next to a well that has been producing for ten years. The design can be deployed in new wells that are drilled next to depleted wells to optimize the system’s oil production.

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“…These solutions are characterized by a position and a velocity [46]. Three parameters mainly control the algorithm [47]:…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study

Merzoug

Rasouli

2023

Eng

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Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Cayeux

Damski²,

Macpherson

et al. 2022

SPE Drilling &Amp; Completion

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Summary Drilling oil and gas wells is a complex process involving many disciplines and stakeholders. This process occurs in a context where some pieces of information are unknown, or are often incomplete, erroneous, or at least uncertain. Yet, during drilling engineering and construction of a well, drilling data quality and uncertainty are barely addressed in an auditable and scientific way. Currently, there are few or no placeholders in engineering and operational databases to document uncertainty and its propagation. The Society of Petroleum Engineers (SPE) has formed a cross-disciplinary technical subcommittee to investigate how to describe and propagate drilling data quality and uncertainty. The subcommittee is a cooperation between the drilling system automation, wellbore positioning, and drilling uncertainty prediction technical sections. As the topic is vast and complex, a systematic method was adopted, where multiple user stories or pain points were generated and ranked with the most compelling user story analyzed in detail. From this approach, a series of multidisciplinary workflows (drilling data generators) can now be captured and described in terms of data quality and propagation of uncertainty. The paper presents details of one user story focused on capturing the description of the quality and uncertainty of depth measurements. Multiple use cases have been extracted from this single user story exemplifying how multiple stakeholders and disciplines manage, communicate, and understand the notion of wellbore depth and its relative uncertainty. Current data stores have the main objective of recording the results of processes but have very limited capabilities to store how the interdisciplinary processes generated and cross-related these results. The study explores the use of semantic networks to capture those multidisciplinary data relationships. A minimum vocabulary has been created using just a few tens of concepts that has sufficient expressiveness to describe all the extracted use cases, showing that the semantic network method has the potential to describe a broad range of complex drilling-related processes. The study also demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagates between each of those concepts.

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Real-Time Directional Guidance Through Cloud-Based Well Path Optimization

Joshi¹,

Kamyab²,

Braga³

et al. 2023

SPE/IADC International Drilling Conference and Exhibition

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This paper shares an implementation of a cloud-based framework that tracks the well position and, in real-time, recommends corrective actions to ensure that the well efficiently follows the well plan. This directional guidance framework can differentiate between vertical, curve, and lateral sections and modify the recommendations accordingly. This paper will share the methods and results from the case studies to validate the directional guidance framework. This cloud-based directional guidance workflow kicks in as soon as the drilling starts. In real-time, the system tracks the bit position and identifies the active section (vertical, curve, lateral, tangent) for the well. Vertical and lateral sections use recommendations previously reported using particle swarm optimization in SPE-206170 (Cardoso Braga et al., 2021). Equations for curve sections are provided that fit the proposed wellbore trajectory to a 3D spheroid using the current motor yield as calculated using the three most recent slides. A real-time assessment of the estimated actual landing point is presented, and warnings of missing the planned landing point are provided. The drilling guidance algorithm was tested for individual sections (vertical, tangent, curve, lateral) on three wells. The recommendations were evaluated to ensure they met each section's optimization goals. The optimization goals for the straight sections are to maximize the ROP, maximize the footage in the window, and minimize tortuosity. Weighting factors for each goal adjust the optimum recommendations based on user requirements. The optimization goals for the curve section are to minimize the distance between the planned landing point and the recommended landing points Then, the guidance system was tested on the complete wells to ensure that the algorithm could correctly identify the section and use the appropriate method. The recommendations from all three wells were evaluated to confirm that the recommendations met the specific criteria applied. Close attention was paid to the transition zones between various sections. The directional guidance workflow resulted in real-time recommendations throughout the well profile for all sections. It was consistently able to output specific steps that the directional driller can take to optimally get closer to the plan. This paper follows up on the previous publications on directional guidance by the authors (SPE 206170 (Cardoso Braga et al., 2021) and SPE 204065 (Cardoso Braga et al., 2021).). It completes the loop on automated directional guidance by adding the missing piece of directional guidance in the curve section and handling transitions between straight (vertical, lateral, tangent) and curve sections. This enables cloud-based automated directional guidance for the entirety of the drilling process.

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Using Particle Swarm Optimization to Compute Hundreds of Possible Directional Paths to Get Back/Stay in the Drilling Window

Cited by 3 publications

References 16 publications

Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study

Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

Real-Time Directional Guidance Through Cloud-Based Well Path Optimization

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