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The most important mechanical properties of casing and tubing are burst strength, collapse resistance and tensile strength. These properties are necessary to determine the strength of the pipe and to design a casing string. If casing is subjected to internal pressure higher than external, it is said that casing is exposed to burst pressure loading. Burst pressure loading conditions occur during well control operations, casing pressure integrity tests, pumping operations, and production operations. The MIYP of the pipe body is determined by the internal yield pressure formula found in API Bull. This equation, commonly known as the Barlow equation, calculates the internal pressure at which the tangential (or hoop) stress at the inner wall of the pipe reaches the yield strength (YS) of the material.
To evaluate a given casing design, a set of loads is necessary. Casing loads result from running the casing, cementing the casing, subsequent drilling operations, production and well workover operations. Internal pressure loads result from fluids within the casing and are modeled with pressure distributions. Pressure distributions are typically used to model the internal pressures. These pressure distributions are discussed next.
AlBahrani, Hussain (Texas A&M University (Corresponding author) | Papamichos, Euripides (email: email@example.com)) | Morita, Nobuo (Aristotle University of Thessaloniki Greece and SINTEF Petroleum Research)
Summary The petroleum industry has long relied on predrilling geomechanics models to generate static representations of the allowable mud weight limits. These models rely on simplifying assumptions such as linear elasticity, a uniform wellbore shape, and generalized failure criteria to predict failure and determine a safe mud weight. These assumptions lead to inaccurate results, and they fail to reflect the effect of different routing drilling events. Thus, this paper’s main objective is to improve the process for predicting the wellbore rock failure while drilling. This work overcomes the limitations by using a new and integrated modeling scheme. Wellbore failure prediction is improved through the use of an integrated modeling scheme that involves an elasto-plastic finite element method (FEM) model, machine learning (ML) algorithms, and real-time drilling data, such as image logs from a logging while drilling (LWD) tool that accurately describes the current shape of the wellbore. Available offset well data are modeled in the FEM code and are then used to train the ML algorithms. The produced integrated model of FEM and ML is used to predict failure limits for new wells. This improved failure prediction can be updated with the occurrence of different drilling events such as induced fractures and wellbore enlargements. The values are captured from real-time data and reflected in the integrated model to produce a dynamic representation of the drilling window. The integrated modeling scheme was first applied to laboratory experimental results to provide a proof of concept and validation. This application showed improvement in rock-failure prediction when compared with conventional failure criteria such as Mohr-Coulomb. Also, offset-well data from wireline logging and drilling records are used to train and build a field-based integrated model, which is then used to show that the model output for a separate test well reasonably matches the drilling events from the test well. Application of this integrated model highlights how the allowable mud-weight limits can vary because drilling progresses in a manner that cannot be captured by the conventional predrilling models. As illustrated by a field case, the improvement in failure prediction through this modeling scheme can help avoid nonproductive time events such as wellbore enlargements, hole cleaning issues, pack-offs,stuck-pipe, and lost circulation. This efficiency is to be achieved by a real-time implementation of the model where it responds to drilling events as they occur. Also, this model enables engineers to take advantage of available data that are not routinely used by drilling.
Abstract Controlling excessive water production in mature oil fields has always been one major objective of the oil and gas industry. This objective calls for planning of more effective water-control treatments with optimized designs to obtain more attractive outcomes. Unfortunately, planning such treatments still represents a dilemma for conformance experts due to the lack of systematic design tools in the industry. This paper proposes and makes available a new design approach for bulk gel treatments by grouping designs of 62 worldwide field projects (1985-2018) according to gel volume-concentration ratio (VCR). After compiling them from SPE papers, the average gel volumes and polymer concentrations in the field projects were used to evaluate the gel VCR. Distributions of field projects were examined according to the gel VCR and the formation type using stacked histograms. A comprehensive investigation was performed to indicate the grouping criterion and design types of gel treatments. Based on mean-per-group strategy, the average VCR was estimated for each channeling and formation type to build a three-parameter design approach. Two approximations for the average polymer concentration and two correlations for minimum and maximum designs and were identified and included in the approach. The study shows that the gel VCR is a superior design criterion for in-situ bulk gel treatments. Field applications tend to aggregate in three project groups of clear separating VCR cut-offs (<1, 1-3, >3 bbl/ppm). The channeling type is the dividing or distributing criterion of the gel projects among the three project groups. We identified that VCRs<1 bbl/ppm are used to treat conformance problems that exhibit pipe-like channeling usually presented in unconsolidated and fractured formations with very long injection time (design type I). For fracture-channeling problems frequently presented in naturally or hydraulically-fractured formations, VCRs of 1-3 bbl/ppm are used (design type II). Large gel treatments with VCR>3 bbl/ppm are performed to address matrix-channeling often shown in matrix-rock formations and fracture networks (design type III). Results show that the VCR approach reasonably predicts the gel volume and the polymer concentration in training (R of 0.93 and 0.67) and validation (AAPE <22%) samples. Besides its novelty, the new approach is systematic, practical, and accurate, and will facilitate the optimization of the gel treatments to improve their performances and success rate.
Understanding rock properties and how they react under various types of stress is important to development of a geomechanical model before drilling. Some major geomechanical rock properties are described below. To first order, most rocks obey the laws of linear elasticity. In other words, the stress required to cause a given strain, or normalized length change (Δlk /ll), is linearly related to the magnitude of the deformation and proportional to the stiffnesses (or moduli), Mijkl. Furthermore, the strain response occurs instantaneously as soon as the stress is applied, and it is reversible--that is, after removal of a load, the material will be in the same state as it was before the load was applied.
Abstract In many drilling scenarios that include deep wells and highly stressed environments, the mud weight required to completely prevent wellbore instability can be impractically high. In such cases, what is known as risk-controlled wellbore stability criterion is introduced. This criterion allows for a certain level of wellbore instability to take place. This means that the mud weight calculated using this criterion will only constrain wellbore instability to a certain manageable level, hence the name risk-controlled. Conventionally, the allowable level of wellbore instability in this type of models has always been based on the magnitude of the breakout angle. However, wellbore enlargements, as seen in calipers and image logs, can be highly irregular in terms of its distribution around the wellbore. This irregularity means that risk-controlling the wellbore instability through the breakout angle might not be always sufficient. Instead, the total volume of cavings is introduced as the risk control parameter for wellbore instability. Unlike the breakout angle, the total volume of cavings can be coupled with a suitable hydraulics model to determine the threshold of manageable instability. The expected total volume of cavings is determined using a machine learning (ML) assisted 3D elasto-plastic finite element model (FEM). The FEM works to model the interval of interest, which eventually provides a description of the stress distribution around the wellbore. The ML algorithm works to learn the patterns and limits of rock failure in a supervised training manner based on the wellbore enlargement seen in calipers and image logs from nearby offset wells. Combing the FEM output with the ML algorithm leads to an accurate prediction of shear failure zones. The model is able to predict both the radial and circumferential distribution of enlargements at any mud weight and stress regime, which leads to a determination of the expected total volume of cavings. The model implementation is first validated through experimental data. The experimental data is based on true-triaxial tests of bored core samples. Next, a full dataset from offset wells is used to populate and train the model. The trained model is then used to produce estimations of risk-controlled stability mud weights for different drilling scenarios. The model results are compared against those produced by conventional methods. Finally, both the FEM-ML model and the conventional methods results are compared against the drilling experience of the offset wells. This methodology provides a more comprehensive and new solution to risk controlling wellbore instability. It relies on a novel process which learns rock failure from calipers and image logs.
Abstract The API equation for internal leak of API connections is uniaxial since it ignores axial force and external backup pressure. ISO 13679 for qualification of premium connections is biaxial at best. It includes tension/compression but ignores backup pressure for both internal and external leak tests. For tubular design, this paper introduces a new fully triaxial safety factor for threaded connections with dependence on thread shear and hydrostatic pressure. Hydrostatic behavior is modelled with the Mean Normal Stress, and thread shear behavior is modelled with the shear component of the von Mises Stress. A Leak Line for use like the pipe body ellipse is proposed for quick leak assessment. Leak ratings are presented for an example case of 7-in. 35-ppf N80 LTC. The new triaxial safety factor with two connection constants applies to all types of threaded connections, including tubing, casing, and drill pipe, so long as the two constants are evaluated with appropriate but simple physical tests.
Borehole instability is the undesirable condition of an openhole interval that does not maintain its gauge size and shape and/or its structural integrity. Figure 1 illustrates hole-instability problems. Hole closure is a narrowing time-dependent process of borehole instability. It sometimes is referred to as creep under the overburden pressure, and it generally occurs in plastic-flowing shale and salt sections. Hole enlargements are commonly called washouts because the hole becomes undesirably larger than intended.
Once a geomechanical model has been developed that quantifies the principal stress magnitudes and orientations, the pore pressure, and the rock properties, it is possible to predict the amount of wellbore instability as a function of mud weight and properties. This makes it possible to reduce drilling costs by keeping lost time low and by designing wells just carefully enough to minimize problems without excessive cost. A further benefit of considering geomechanical risk is that when problems are encountered, their causes can be recognized and plans can be in place to mitigate their effects with minimal disruption of the drilling schedule. Figure 1 shows the time-depth plot of an offshore well that was designed and drilled without the use of geomechanical modeling. After setting the first string to isolate a shallow hazard, the remaining casing depth points were selected based on drilling experience in an offset block, supplemented by pore pressures predicted using seismic data.