Casing wear due to pipe body and tool-joint of Range 2 and Range 3 DP is compared using a stiff-string torque & drag & buckling model coupled to a 3D meshed casing wear calculation. Results are compared for multiple well profiles, either smooth or tortuous, in addition to differing pipe-body and tool-joint wear factors.

Casing wear predictions are necessary for a fit-for-purpose, cost efficient casing design. In order to achieve a reliable casing wear prediction from simulations, three objectives have to be met: (1) Accurate modelling of mechanical work, (2) accurate derivation of casing wear from mechanical work and (3) accurate wear factor calibration through MFCL (Multi Finger Caliper Log) interpretation. The paper presents a theoretical description of all three steps and their application on a representative field case, highlighting recent improvements and reducing uncertainties at each step with a focus on a newly developed MFCL interpretation methodology. To accurately derive the mechanical work an advanced stiff-string model(3) is used to simulate the history of side forces for each rotating operation, of each BHA, which was in contact with the casing. Accurate surveys and small calculation increments are vital. Operation parameters must be data mined with great care. To properly derive the wear from simulated mechanical work, a 3-D oriented wear model is used. This 3D mesh is able to distinguish the influence of different contact geometries on the wear groove shape and the location of thinnest wall thickness. Due to the usage of an advanced stiff-string model, drillstring body contact points are identified and therefore the wear model allows the input of different wear factors for different types of contacts (Tooljoint, Pipebody, Torque reducer)(12). Additionally, the model allows the application of linear and non-linear wear models. To properly calibrate wear factors, a reliable and robust MFCL log interpretation must be conducted. Wear is commonly measured by comparing the results of a MFCL to a base MFCL, which ideally was taken at the beginning of the section. In the standard process, the maximum measured diameters per joint of both MFCLs are compared. A more accurate single run-MFCL interpretation methodology will be presented, which relies on statistical analysis of the casing shape. It was found that the standard MFCL interpretation methodology may be the weak link in the chain. The standard methodology led to significant exaggeration of the wear factor(s), especially if the calibration was done based on mild levels of wear. Casing wear prediction remains a complex issue, due to uncertainties concerning wear and friction factor. The new MFCL calibration methodology, including a robust stiff-string torque and drag model, is capable of significantly reducing the levels of uncertainty. Overall the complete methodology is the basis for accurate wear prediction and reduces the need of casing over-engineering.

This paper is concerned with portfolio selection for an investor with power utility in multi-asset financial markets in a rough stochastic environment. We investigate Merton's portfolio problem for a class of multivariate affine Volterra models introduced in [5] and a Volterra-Wishart model based on the model described in [3], both covering the rough Heston model. Due to the non-Markovianity of the underlying processes the classical stochastic control approach can not be applied in this setting. To overcome this difficulty, we provide a verification argument inspired by [3] to show that an appropriate candidate is indeed the optimal portfolio strategy using calculus of convolutions and resolvents. The optimal strategy can then be expressed explicitly in terms of the solution of a multivariate Riccati-Volterra equation. This extends the results obtained by Han and Wong to the multivariate case, avoiding restrictions on the correlation structure linked to the martingale distortion transformation used in [9]. We also provide a numerical study to illustrate our results.

The forces and stresses along casing strings are modeled using a stiff string torque and drag model. The effect of wellbore tortuosity and centralization are quantified in preplanning phase in addition to the effect of 3D orientated casing wear. A realistic case study is presented to show the resulting effect on axial, burst, collapse and Von Mises equivalent (VME) safety factor as well as VME body and connection design envelopes. While running a tubular downhole, a smooth wellbore is normally assumed when performing a torque and drag calculation. In reality, the inherent tortuosity of the wellbore which is caused by the drilling process can cause significant local doglegs. When applying a soft-string torque and drag model, the stiffness, radial clearance and high frequency surveys needed to fully model local doglegs are rarely modeled. The stiff string torque and drag and buckling model can model these effects, as well as the addition of rigid and flexible centralisers. This study involves the comparison of different casing design load cases, under different centralizer programs and tortuosity taking into account a 3D orientated casing wear. The results show that there can be significant differences in overall axial stress depending on the centraliser program and tortuosity used. The soft string model doesn't directly account for bending stress, normally this is estimated using a Bending Stress Magnification Factor (BSMF). In contract the stiff string model can directly calculate the additional bending stress. This additional stress can be particularly prevalent while RIH casing with centralisers and high tortuosity. The reduction in American Petroleum Institute (API) and VME stress envelope is also quantified using a 3D orientated casing wear model. A better understanding of axial stress state reduces risk of well integrity issues. This paper will show the benefits of using a stiff string model, considering additional contact points, bending stress as well as the benefits of modelling tortuosity and centralizer program early in the design process. During extended reach drilling (ERD) and high-pressure, high temperature (HPHT) wells, this information can be critical when correctly assessing the axial stress state.