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ABSTRACT: Geomechanical properties are important for reservoir characterization and optimal stimulation design in the oil and gas industry. The conventional techniques, such as laboratory core analysis and downhole acoustic/wireline logging can be expensive and sometimes uncertain to process for unconventional reservoirs. In this study, a convenient and cost-effective technology is presented that uses routinely available drilling data to calculate the geomechanical properties without the need for downhole logging operations. A wellbore friction model is used to estimate the coefficient of friction and effective downhole weight on bit (DWOB) from the routinely collected drilling data. The inverted rate of penetration (ROP) models use the estimated downhole weight on bit and formation lithology constants to calculate the geomechanical properties throughout the horizontal reservoir formations such as confined compressive strength (CCS), unconfined compressive strength (UCS), Young’s modulus, permeability, porosity and Poisson’s ratio. In this article, the field case study is presented for a sample North American well applied to the lower Eagle Ford formation. The calculated geomechanical property log is also verified with tests performed on cores in reservoir rock formations.
Continuous monitoring of rock mechanical and reservoir properties along the wellbore in unconventional horizontal wells demands convenient and efficient logging techniques. The conventional logging techniques involve laboratory core analysis and well logging using sonic and resistivity image logs which are not readily available for all unconventional wells (1 in 10 or 1 in 20) mainly due to associated cost, data uncertainity and time consuming to process. Moreover there are possible risks and concerns of trapping logging tools downhole in highly deviated and horizontal wells drilled in unconventional reservoirs. For many years, researchers and engineers have been investigating several models and techniques to obtain geomechanical property logs for the successful development of unconventional resrvoirs and stimulation design for maximum hydrocarbon production. The Artificial Intelligence and Data Mining (AI&DM) or data- driven models were developed to generate synthetic geomechanical information from the conventional logs in shale plays (Eshkalak et al., 2013). The conventional log data from a shale well was used for training and calibration during neural network model development to generate the synthelic logs for other wells. This model provides better performance for the wells in proximity of the training well with actual geomechanical properties. A convenient ROP model was developed to calculate rock mechanical properties such as, confined compressive strength (CCS), unconfined compressive strength (UCS) and Young’s modulus (E) at each drilled depth from the routinely collected drilling data such as rate of penetration (ROP), weight on bit (WOB) and RPM (Hareland and Nygaard, 2007). In horizontal drilling, the actual downhole weight on bit differs from the measured surface WOB (obtained from on and off bottom hook load difference readings) due to the friction caused by drill string movement, rotation within the wellbore and wellbore geometry. A previously developed 3D wellbore friction model (torque and drag (T&D) model) was used to estimate the coefficient of friction and effective downhole weight on bit (DWOB) from the surface measurements of WOB, hook load, surface applied RPM along with the wellbore survey measurement, standpipe pressure and drill string information (Fazalizadeh et al., 2010).
Abstract The length and inclination of wells have increased significantly the past decades. The record horizontal departure today is about 11 km from the platform. Because of this evolution the number of offshore platforms to drain a field has been significantly reduced. Excessive torque and drag can be critical limitations in extended-reach drilling (ERD). Torque and drag modeling is regarded as an invaluable process to assist in well planning and to predict and prevent drilling problems. It discusses how to use torque and drag calculations and measurements to plan long-reach well profiles, to execute drilling operations that minimize torque and drag effects, to monitor hole cleaning, and to plan jarring operations. Torque and drag models have proven to be useful in all three stages of an extended reach well: planning, drilling and post-analysis. During planning phase the models are used to optimize the trajectory design to minimize the torque, drag and contact forces between drillstring and borehole wall. Used together with monitoring of hole conditions during drilling, T&D models are particularly useful in diagnosing hole cleaning problems, impending differential sticking, and severe doglegs as well as determining the possibility of reciprocating casing during cementing operations. In post-analysis the models help to determine true causes of hole problems that previously were unexplained or attributed to other factors such as mud weight, mud chemistry or problem shales. In this study a general overview on most of the available literature on the subject is presented. Different models that have been developed for torque and drag predictions along with pros and cons of the models will be discussed and the validity will be checked by applying the model for one field case study from an ERD well in North Sea. The field case also demonstrates the challenges and importance of buoyancy effects and well path effects.
ABSTRACT In this paper, a novel rate of penetration (ROP) model based on rock cutter interaction is introduced. ROP models for polycrystalline diamond compact (PDC) bits have been developed and improved by many researchers, however most of these models ignore the interfacial friction angle (IFA) on ROP estimation. This shortcoming cause inaccuracy of ROP estimation in many operational situations and in different formations. The new PDC ROP model includes the interfacial friction between the cutter face and rock. An analytical equation for single cutter front area is presented and used as a base for developing the ROP model for a PDC drill bit. The IFA model for PDC drill bits was developed fitting full hole drilling laboratory data using the differential evolution algorithm (DEA). Both the PDC drill bit IFA and the ROP models for a PDC drill bit were verified using laboratory experimental data for a different bit diameter, back rake angle (BR), operational parameters, and rock strength. Drilling simulation, autonomous drilling decision making, and real-time optimization are potential applications of the model. 1. INTRODUCTION Drilling simulation software programs are important cost-saving technology tools in well planning and drilling operations. The core engine for these simulators are the ROP models that use geological data, drilling operational parameters, and drill bit details as inputs and provide an estimation for ROP as an output. Due to the complexity of the cutting action of PDC cutters especially in hard rocks and without having an accurate ROP estimation tool, it is unlikely to achieve an efficient drilling process In this study, the explicit mathematical equations of a single cutter front area are presented and used for the development of a novel ROP model for PDC bits. The PDC cutter size, cutter BR, speed, and depth of cut (δ) are among the important parameters that influence the cutting mechanisms and consequently the ROP.
Pastusek, Paul (ExxonMobil Upstream Research Co.) | Payette, Greg (University of Calgary) | Shor, Roman (Norce) | Cayeux, Eric (Brigham Young University) | Aarsnes, Ulf Jakob (Brigham Young University) | Hedengren, John (DrillScan) | Menand, Stéphane (Baker Hughes GE) | Macpherson, John (MindMesh Inc.) | Gandikota, Raju (Apache Corp.) | Behounek, Michael (Schlumberger) | Harmer, Richard (University of Minnesota) | Detournay, Emmanuel (Integrity Directional) | Illerhaus, Roland (Shell Development Co.) | Liu, Yu (Shell Development Co.)
Abstract The drilling industry has substantially improved performance based on knowledge from physics-based, statistical, and empirical models of components and systems. However, most models and source code have been recreated multiple times, which requires significant effort and energy with little additional benefit or step-wise improvements. The authors propose that it is time to form a coalition of industry and academic leaders to support an open source effort for drilling, to encourage the reuse of continuously improving models and coding efforts. The vision for this guiding coalition is to 1) set up a repository for source code, data, benchmarks, and documentation, 2) encourage good coding practices, 3) review and comment on the models and data submitted, 4) test, use and improve the code, 5) propose and collect anonymized real data, 6) attract talent and support to the effort, and 7) mentor those getting started. Those interested to add their time and talent to the cause may publish their results through peer-reviewed literature. Several online meetings are planned to create this coalition, establish a charter, and layout the guiding principles. Multiple support avenues are proposed to sustain the effort such as: annual user group meetings, create a SPE Technical Section, and initiating a Joint Industry Program (JIP). The Open Porous Media Initiative is just one example of how this could be organized and maintained. As a starting point, this paper reviews existing published drilling models and highlights the similarities and differences for commonly used drillstring hydraulics, dynamics, directional, and bit-rock interaction models. The key requirements for re-usability of the models and code are: 1) The model itself must be available as open source, well documented with the objective and expected outcomes, include commented code, and shared in a publicly available repository which can be updated, 2) A user's guide must include how to run the core software, how to extend software capabilities, i.e., plug in new features or elements, 3) Include a "theory" manual to explain the fundamental principles, the base equations, any assumptions, and the known limitations, 4) Data examples and formatting requirements to cover a diversity of drilling operations, and 5) Test cases to benchmark the performance and output of different proposed models. In May 2018 at "The 4th International Colloquium on Non-linear dynamics and control of deep drilling systems," the keynote question was, "Is it time to start using open source models?" The answer is "yes". Modeling the drilling process is done to help drill a round, ledge free hole, without patterns, with minimum vibration, minimum unplanned dog legs, that reaches all geological targets, in one run per section, and in the least time possible. An open source repository for drilling will speed up the rate of learning and automation efforts to achieve this goal throughout the entire well execution workflow, including planning, BHA design, real-time operations, and post well analysis.
Summary Reamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology. Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning. Separate ROP models are trained for reamer‐controlled and bit‐controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit‐reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well. The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.