SummaryThe main objective of this paper is to present and discuss the results and significant observations gathered during 13 experimental runs conducted in a full-scale test well at Louisiana State University (LSU). The other two objectives of this manuscript are to show the use of distributed fiber-optic sensing and downhole pressure sensors data to detect and track the gas position inside the test well during the experiments, and to discuss experimental and simulated data of the gas migration phenomenon in a closed well.An existing test well at LSU research facilities was recompleted and instrumented with fiber-optic sensors to continuously collect downhole data and with four pressure and temperature downhole gauges at four discrete depths within an annulus formed by 9 5/8 in. casing and 2 7/8 in. to a depth of 5,025 ft. A chemical line was attached to the tubing allowing the nitrogen injection at the bottom of the hole. The research facilities were also equipped with a surface data acquisition system. The experiments consisted in injecting nitrogen into the test well filled with water by two means: either injecting it down through the chemical line or down through the tubing to be subsequently bullheaded to the annulus. Afterward, either the nitrogen was circulated out of the well with a backpressure being applied at surface to mimic a managed pressure drilling (MPD) operation or left to migrate to the surface with the test well closed.During the runs, the three acquisition systems (fiber optic, downhole gauges, and surface data acquisition) recorded all relevant well control parameters for a variety of gas injected volumes (2.0–15.1 bbl), circulation rates (100–300 gal/min), and applied backpressures (100–300 psi). The experimental results gathered by the acquisition systems were very consistent in measuring gas velocities inside the well. The numerical model predictions matched very close to the pressure behavior observed in the experimental trials. In the gas migration experiments, it was observed that the stabilized casing pressure at the end of gas migration is less than the initial bottomhole pressure, and it is a function of the volume of gas injected in the well. These facts are supported by the numerical simulation results.In this paper, we show the possibility of the use of fiber-optic and downhole pressure sensors information to detect and track the gas position inside a well or the marine riser during normal or MPD operations. Additionally, the vast amount of experimental data gathered during the experiments in which the nitrogen was left in the closed well to migrate to surface helped shed light on the controversial issue concerning the surface pressure buildup while the gas migrates to surface in a closed well. Numerical simulations were all instrumental for supporting the findings.
By creating a reverse deflection, negative-displacement horizontal well technology can successfully address the issue of construction challenges brought on by too little displacement in front of the horizontal well target. However, conventional horizontal well casing string running models are unsuitable for negative-displacement horizontal wells. This is because conventional horizontal well models assume that the casing string is in contact with the lower side of the borehole, which is inconsistent with the actual situation in negative -displacement horizontal wells. In this study, we examine the effects of fluid viscous resistance and internal and external fluid interaction forces on the running of the casing string using the Gaussian method and the complementary surface equivalence method. Based on the bending beam theory, we establish a deflection model to examine the interaction between the casing string and the borehole wall in the bending section. The friction and hookload calculation models of each well section and the strength check models are also established. The calculation results show that, according to the aforementioned model, there is an average error of 7.5% between the measured data and the calculated frictional force of the H1 and H2 negative-displacement horizontal wells. This error is within the reasonable range of field application and attests to the validity of the theoretical model. Finally, we study the influence of running factors on running ability and strength of casing string using the control variable method. The results indicate that the weight of the casing string is positively correlated with the variation law of the running ability of the casing string, the maximum offset distance and the length of the horizontal section are negatively correlated with the running ability of the casing string, and the borehole curvature is positively correlated with the variation law of the connection stress of the internal and external threads of the casing string. Therefore, in the process of running the cas-ing string in negative-displacement horizontal wells, using heavier casing string and reducing the maximum offset distance and horizontal section length can improve the running ability of the casing string.
A crucial step in new product development is the design verification process (DVP), which assures that the conceptual design of new technology is successfully transformed into a prototype product. In this paper, a novel DVP for subsea electrical and electronic prototype products is introduced. The comprehensive DVP ensures that the prototype of the product is designed righteously, is well documented, and is verified according to international practices and standards such as The American Petroleum Institute (API), International Organization for Standardization, European Norm, and European Union specifications. Compliance testing [environmental stresses and electromagnetic compatibility (EMC)] on the product is proposed to achieve Technology Readiness Level 2 (TRL2) of the first article of a printed circuit board (PCB). Concerning the quality of an electronic circuit board, it is recommended in this paper to follow the guide-lines and specifications of the Institute of Printed Circuits to certify the robustness of the design. Finally, the proposed DVP is thoroughly exercised on the insulation monitoring PCB under the prescribed test methods as a case study. The experimental results show that the design is robust and the product is suitable for use in the intended subsea environment after further raising the technology maturity level of the product.
SummaryThe experimental design of well cement with durable compressive strength (CS) is challenging and time-consuming. The current research predicts CS using the enhanced group method of data handling via a modified Levenberg-Marquardt algorithm (GMDH-LM) with experimental data. Class F fly ash (CFFA) is used as a supplementary material to cement at various proportions. Experimental tests of CS, thermogravimetric (TG) analysis, rheology, and scanning electron microscopy (SEM) are applied. Experimental findings revealed that the addition of fly ash (FA) enhances CS with curing time as an outcome of pozzolanic action. CS for 20% FA reinforcement after curing for 28 days was 42.95 MPa, compared with 41.53 MPa for 50%. This indicates that a higher addition of FA lowers CS. The rheological findings revealed that FA enhanced the viscosity of the cement slurry. The SEM images demonstrated that the incorporation of CFFA with cement modified the contexture of hardened cement. Cement, water, oilwell cement (OWC), curing time, dispersant, and FA were assigned as input variables for GMDH-LM while CS from the experimental analysis was set as output. Machine learning (ML) findings indicated that GMDH-LM can effectively estimate the CS of OWC. GMDH-LM performed better than backpropagation neural network (BPNN), support vector machine (SVM), and normal GMDH models in predicting CS; it provided higher linearity during training as GMDH-LM gave R2 = 0.958, GMDH = 0.946, SVM = 0.925, BPNN = 0.897, and the least loss functions of mean square error (MSE) = 0.238, MSE = 1.685, MSE = 2.567, and MSE = 4.032, respectively. Similarly, good results were ascertained during testing GMDH-LM provided R2 = 0.928, GMDH = 0.907, SVM = 0.895, BPNN = 0.878, and the lowest loss functions of MSE = 0.304, MSE = 2.650, MSE = 3.494, and MSE = 5.678, respectively. Therefore, the comparative results of all experiments and predictions reveal that GMDH-LM can be deployed as an advanced approach for the estimation of cement hydration in oil and gas wells.
The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process.The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high- quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time.The tool consists of a training and a prediction tool integrated with cased- hole logging interpretation software. By containerizing the code using an "API First" design principle (API: application programming interface), the applicability of this add- on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further.To improve cement log interpretation consistency in the industry, the code will be made available as open source.
