fax 01-972-952-9435. AbstractThis paper presents a method to determine optimum drilling fluid properties and flow rates to minimize cuttings bed height and circulation time in high angle and horizontal wells. The method uses empirical models relating the cuttings bed height and the bed erosion time to drilling fluid properties and flow rates.Bed erosion tests have been conducted using a cuttings transport facility available at the University of Tulsa. Cuttings bed height as a function of time has been investigated by using variable flow rates (200 -400 gpm) and four different drilling fluid compositions. Experimental results were used together with a non-linear regression analysis program to establish a functional relationship among drilling fluid properties, flow rate, cuttings bed height and the time required to circulate the borehole clean.A numerical example is provided to explain the field application of the method. The sequential calculations involved in determining optimum combination of the Power Law viscosity parameters n and K, and the flow rate to minimize the cuttings bed height and circulation time are also given.Field implementation of the proposed empirical correlations and the new method can aid optimization of circulation practices before tripping, and so reduce the associated risk of nonproductive time.
Riserless drilling poses numerous operational challenges that manifests itself in a number of ways, that can adversely affecting the efficiency of the drilling process. The problems include increased torque and drag, increased vibration, poor hole cleaning, tubular failures by buckling above the mud line, poor cement jobs, and associated problems during tripping operations. Drilling in deepwater and ultra-deepwater as well as extending the reach to a greater along hole depth in the riserless environment requires both improved models and comprehensive analysis, especially when the larger diameter casing pipes are run and cemented. The present calculations without proper modeling will gravely underestimate the hook load values when the casing strings are run in deepwater situations. This paper proposes a modeling approach, which uses scenarios in which the drillstring/casing strings are in open water and in openhole reservoirs under different operating conditions to arrive at appropriate hook-load values in addition to torque and drag calculations. Both combinations of soft and stiff string models are used for the tension-force estimation as well as the wellhead-side loading calculations. The research results also present the hook-load calculations for scenarios when casing and inner string are run with drilling mud inside the inner string, sea water in the outer string, and pad mud in the hole below the mud line. The study concludes that various parameters influence the results, such as wellhead offset from the rig center, wellbore inclination, curvature, wellbore torsion, angle of entry into the wellhead besides the complexity from wind, wave forces, and ocean currents. This paper documents the comparison between the predicted mathematical simulation results with the actual well data from different wells to explain the rigor of implementation.
Environmental impacts due to exploration to the abandonment of oil and gas wells are a major focus when the wells are planned and drilled. However, there is no industry standard to quantify the impact due to complex indicators and associated variables. So, it is necessary to have an assessment framework and a methodology with the help of the digital transformation that provides an integrative index. This index allows a baseline to estimate the benchmarking for the well engineering not only during planning but also in real-time as the well is drilled and produced. This paper presents and validates a new model for the sustainability index for well design and engineering in the life cycle of the well. This proposed method avoids some of the vagueness of well’s sustainability and can be used and applied practically. It is based on various metrics and weightage assigned when a well is planned, designed, and engineered. Evaluating the index for well engineering is based on the following elements: environmental impact, well design and engineering, functionality and optimization impact, impact of well and maintenance costs, health and safety impact, societal impact. Each element contains sub-elements. This process involves individual indexing through backpropagation of neural networks combined with bat algorithm to obtain the final overall index.
The environmental impact of oil and gas wells from exploration to abandonment is a primary focus during well planning and drilling. However, there is no industry standard to quantify the impact of complex indicators and associated variables, particularly when the well is drilled in real time. Thus, a quantification method using the associated variables used for real-time well monitoring as the well is drilled and produced can be useful. A new model for the sustainability index for well design and engineering during drilling is presented and validated. This proposed method avoids some of the vagueness of well sustainability and can be used and applied in a practical manner. It is based on various metrics and weightage assigned when a well is drilled and compared to the planned metrics. Evaluating the index for well engineering is based on elements of environmental impact, well design and engineering, the impact of functionality and optimization, well and maintenance costs, health and safety, and societal impact. Each element contains subelements. This process involves individual indexing through backpropagation of neural networks combined with the bat algorithm to obtain the echo location to identify the overall index. The proposed method uses a well-engineering-based approach that defines boundaries and thresholds as the well is drilled. The Powell Method, designed for nonconstraint optimization, is also used for fast convergence for this derivative-free problem. The algorithm is fast and provides results based on an iteration method. Because engineering during the well lifecycle involves several nonlinear system and asymmetric inputs, it has been observed that identifying the effects of input uncertainties and other related calculation uncertainties (i.e., variation and errors in log data, survey data, etc.) create an effect. It was also observed that uncertainty analysis provided an optimized index without assigning preferential weightage to some of the components. The method does not reduce uncertainty, but can be used to estimate the influence of various parameters on the sustainability index. To achieve runaway uncertainty, a mutation strategy is introduced to maintain the population diversity of the indices within the defined limits.
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