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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.
As installed, casing usually hangs straight down in vertical wells or lays on the low side of the hole in deviated wells. Thermal or pressure loads might produce compressive loads, and if these loads are sufficiently high, the initial configuration will become unstable. However, because the tubing is confined within open hole or casing, the tubing can deform into another stable configuration, usually a helical or coil shape in a vertical wellbore or a lateral S-shaped configuration in a deviated hole. These new equilibrium configurations are what we mean when we talk about buckling in casing design. In contrast, conventional mechanical engineering design considers buckling in terms of stability (i.e., the prediction of the critical load at which the original configuration becomes unstable).
Introduction The three primary functions of a drilling fluid--the transport of cuttings out of the wellbore, prevention of fluid influx, and the maintenance of wellbore stability--depend on the flow of drilling fluids and the pressures associated with that flow. For example, if the wellbore pressure exceeds the fracture pressure, fluids will be lost to the formation. If the wellbore pressure falls below the pore pressure, fluids will flow into the wellbore, perhaps causing a blowout. It is clear that accurate wellbore pressure prediction is necessary. To properly engineer a drilling fluid system, it is necessary to be able to predict pressures and flows of fluids in the wellbore. The purpose of this chapter is to describe in detail the calculations necessary to predict the flow performance of various drilling fluids for the variety of operations used in drilling and completing a well. Overview Drilling fluids range from relatively incompressible fluids, such as water and brines, to ...
An exceptional flow case is the operation of running pipe or casing into the wellbore. Moving pipe into the wellbore displaces fluid, and the flow of this fluid generates pressures called surge pressures. When the pipe is pulled from the well, negative pressures are generated, and these pressures are called swab pressures. In most wells, the magnitude of the pressure surges is not critical because proper casing design and mud programs leave large enough margins between fracture pressures and formation-fluid pressures. Typically, dynamic fluid flow is not a consideration, so a steady-state calculation can be performed.
Abstract All wells require casing strings so that the planned operations can proceed. Ensuring a good quality casing set is vitally important. When conducting the calculations for frictional pressure losses the casing couplings are not taken into consideration. In API calculation methodologies for drill pipe the effect of tool joints is not taken into calculation. However, the small clearance between the casing coupling and the hole size is definitely creating an additional frictional pressure drop in comparison to the calculated which under normal circumstances taken into account the nominal casing outer diameter (OD). In this study the effect of casing couplings is taken into consideration when calculating the annular frictional pressure losses to drive the Equivalent Circulating Density (ECD). The generally accepted frictional pressure loss equations are used for a variety of casing running scenarios. The methodology that is introduced in this research study is a step change for automation in drilling operations. The findings are used to compare with the conditions during which the effect of casing couplings is not taken into consideration. The general findings indicate that annular frictional pressure losses are very critical for all wells but especially for the wells with narrow drilling margins. This research study reveals that annular frictional pressure losses are very critical for the successful casing running operations not only during circulations through the casing string but also at the time of the cementing of the same. The introduced methodology that takes into consideration of casing couplings can be used for automation in drilling operations.
Abstract The success or failure of cement plugs are known to alter the timeline of an oil well; not to mention the additional costs and NPT associated with the rig activities. Unsuccessful cement plug costs oil companies considerable amount of capital both in extra rig time and service company expenses. Suggested procedures for placing cement plugs have been presented in number of papers - comprising of slurry design, spacer recommendations, laboratory testing and placement techniques. However, it is very easy to deviate from these standard practices due to over confidence, negligence or both. In Mexico, it was observed that the success rate of placing cement plugs dropped due to operational and engineering design shortcomings. Towards the end of 2018 there were several unsuccessful cement plug jobs that questioned the regular plug procedures. Careful analysis of the past mistakes led to the conclusion that an effective approach to alter the local plug placement practices was necessary. An updated cement plug placement software was used in conjunction with strict standard practices that turned around the trend and enabled consistent successful placement of cement plugs in the first attempt itself. A detailed yet simple approach towards cement plugs was adopted in both engineering design and operational execution. Additionally the updated plug placement software ensured accurate prediction of the cement plug top; that was confirmed by the actual tag of the plug. This paper will enlist the major analysis carried out on the unsuccessful plug jobs and highlight the different techniques that were adopted in the subsequent jobs to ensure successful placement and tagging of the cement plug. The paper will also focus on how the plug placement software's new additional features have made a significant contribution to this success story.
Abstract Using the right drilling fluid with optimal rheology and filtration properties is one of the most important factors in successful drilling and completion operations. Designing the right drilling fluid depends on a variety of factors viz. formation lithology, wellbore geometry, temperature, pressure, and drilling objectives. To the best of the author's knowledge there is no standard drilling fluid advisory system to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections. The paper describes a drilling fluid advisory system based on Artificial Bayesian Intelligence. The advisory system includes a Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. This advisory system has been designed to aid drilling engineers when designing drilling fluids for their operations. This paper describes a module that was created in this advisory system. This module was created based on several inputs viz. well geometry (vertical and horizontal), temperature, pressure, productivity. To create the drilling fluids module within the advisory system, a number of drilling fluid specialists/experts were interviewed to gather the information required to determine the best practices as a function of the above inputs. These best practices were then used to build decision trees that would allow the user to take an elementary data set and end up with a decision that honors the best practices. The designing process of this advisory system also included a number of standard lab tests that start from quality assurance, initial designing and finally using field samples to confirm the success of the application. The study also discusses several field cases that validate the drilling fluids advisory system. The novel drilling fluid advisory system based on Artificial Bayesian Intelligence has been designed to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections.
Abstract During a drilling operation, rock cuttings are often sampled off a shale shaker for lithology and petrophysical characterization. These analyses play an important role in describing the subsurface; and it is important that the depth origin of the cuttings be accurately determined. Traditionally, mud-loggers determine the depth origin of the sampled cuttings by calculating the lag time required for the cuttings to travel from the bit to the surface. These calculations, however, can contain inaccuracies in the depth correlation due to the shuffling and settling of cuttings as they travel with drilling fluid to the surface, due to unplanned conditions like drilling an overgauge hole, and due to other unforeseen drilling events, especially critical in horizontal sections. We therefore aimed to remedy these inaccuracies by developing a series of styrene-based nanoparticles that tagged the cuttings as they were generated at the drillbit. These “NanoTags” were tested while drilling in Q4, 2019; and the results indicated that the NanoTags did in fact have the potential to identify some systematic errors compared with traditional mud logging calculations.
Abstract Fluid saturation data obtained from core analysis are used as control points for log calibration, saturation modeling and sweep evaluation. These lab-derived data are often viewed as ground-truth values without fundamentally understanding the key limitations of experimental procedures or scrutinizing the accuracy of measured lab data. This paper presents a unique assessment of sponge core data through parameterization, uncertainty analysis and Monte-Carlo modeling of critical variables influencing lab-derived saturation results. This work examines typical lab data and reservoir information that could impact final saturation results in sponge coring. We dissected and analyzed ranges of standard raw data from Dean-Stark and spectrometric analysis (including, gravimetric weights, distilled water volumes, pore volumes and sponge’s absorbance), input variables of fluid and rock properties (such as, water salinity, formation volume factors, plug’s dimension and stress corrections), governing equations (including, salt correction factors, water density correlations and lab mass balance equations) and other factors (for instance, sources of water salinity, filtrate invasion, bleeding by gas liberation and water evaporation). Based on our investigation, we have identified and statistically parameterized 11 key variables to quantify the uncertainty in lab-derived fluid saturation data in sponge cores. The variables’ uncertainties were mapped into continuous distributions and randomly sampled by Monte-Carlo simulation to generate probabilistic saturation models for sponge cores. Simulation results indicate the significance of the water salinity parameter in mixed salinity environments, ranging between 20,000 to 150,000 ppm. This varied range of water salinity produces a wide uncertainty spectrum of core oil saturation in the range of +/- 3 to 10% saturation unit. Consequently, we developed two unique salinity variance models to capture the water salinity effect and minimize the uncertainty in the calculation of core saturation. The first model uses a material balance to solve for the salinity given the distilled water volume and gravimetric weight difference of the sample before and after leaching. The second model iteratively estimates the salinity required to achieve 100% of total fluids saturation at reservoir condition after correcting for the bleeding, stress and water evaporation effects. Our work shows that these derived models of water salinity are consistent with water salinity data from surface and bottom-hole samples. Despite the prominence of applications of core saturation data in many aspects of the industry, thorough investigation into its quality and accuracy is usually overlooked. To the best of our knowledge, this is the first paper to present a novel analysis of the uncertainty coupled with Monte-Carlo simulation of lab-derived saturation’s data from sponge cores. The modeling approach and results highlighted in this work provide the fundamental framework for modern uncertainty assessment of core data.
Abstract This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical responses to the lithology. The Sw model in this field showed certain log readings (high resistivity [Rt] reading ≥ 500 ohm.m) that infer these intervals to be probable inverse-wet (oil-wet). The cross-plots (Rt vs. Vcl; Rt vs. density [RHOB]; Rt vs. total porosity [PHIT]) were used to discard the lithologies related to a high Rt (e.g., lignites and calcareous rocks) and to correct Sw when these resulted in values below the estimated irreducible water saturation (Swir). The Sw calculations using the Indonesian equation were updated to incorporate n as a variable (log curves), comparing it with Sw from the core data and previous calculations using a fixed average value (n = 1.82) from the core data. An integrated approach was used to determine n, which is related to the reservoir’s heterogeneity and wettability changes. The values of n for high Rt (n > 2) intervals ranged from 2.3 to 8.5, which is not close to the field average n value (1.82). Specific correlations were found by discriminating Swir (Swir < 15%), (Swir 15%–19%), and Swir (> 19%). The results showed that using n as a variable parameter improved Sw from 39.5% to 36.5% average in the T1 and T2 sands, showing a better fit than the core data average and increasing the STOIIP estimations by 6.81%. This represents now a primary oil recovery of 12.1%, closer to the expected value for these reservoirs. Although many studies have been done on n determination and its effect on Sw calculations, using average values over a whole field is still a common practice regardless of heterogeneity and wettability considerations. This study proposed a method to include the formation of heterogeneity and wettability changes in n determination, allowing a more reliable Sw determination as demonstrated in the TAM oil field in Suriname.