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Pan, Jin (Wuhan University of Technology, Wuhan) | Wang, Tao (Wuhan University of Technology, Wuhan) | Xu, Ming Cai (Huazhong University of Science and Technology, Wuhan / Collaborative Innovation Centre for Advanced Ship and Deep-Sea Exploration) | Gao, Gui (Wuhan-Jiujiang Railway Passenger Transportation Hubei Co. Ltd.)
The hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.
Nicholson, A. Kirby (Pressure Diagnostics Ltd.) | Bachman, Robert C. (Pressure Diagnostics Ltd.) | Scherz, R. Yvonne (Endeavor Energy Resources) | Hawkes, Robert V. (Cordax Evaluation Technologies Inc.)
Abstract Pressure and stage volume are the least expensive and most readily available data for diagnostic analysis of hydraulic fracturing operations. Case history data from the Midland Basin is used to demonstrate how high-quality, time-synchronized pressure measurements at a treatment and an offsetting shut-in producing well can provide the necessary input to calculate fracture geometries at both wells and estimate perforation cluster efficiency at the treatment well. No special wellbore monitoring equipment is required. In summary, the methods outlined in this paper quantifies fracture geometries as compared to the more general observations of Daneshy (2020) and Haustveit et al. (2020). Pressures collected in Diagnostic Fracture Injection Tests (DFITs), select toe-stage full-scale fracture treatments, and offset observation wells are used to demonstrate a simple workflow. The pressure data combined with Volume to First Response (Vfr) at the observation well is used to create a geometry model of fracture length, width, and height estimates at the treatment well as illustrated in Figure 1. The producing fracture length of the observation well is also determined. Pressure Transient Analysis (PTA) techniques, a Perkins-Kern-Nordgren (PKN) fracture propagation model and offset well Fracture Driven Interaction (FDI) pressures are used to quantify hydraulic fracture dimensions. The PTA-derived Farfield Fracture Extension Pressure, FFEP, concept was introduced in Nicholson et al. (2019) and is summarized in Appendix B of this paper. FFEP replaces Instantaneous Shut-In Pressure, ISIP, for use in net pressure calculations. FFEP is determined and utilized in both DFITs and full-scale fracture inter-stage fall-off data. The use of the Primary Pressure Derivative (PPD) to accurately identify FFEP simplifies and speeds up the analysis, allowing for real time treatment decisions. This new technique is called Rapid-PTA. Additionally, the plotted shape and gradient of the observation-well pressure response can identify whether FDI's are hydraulic or poroelastic before a fracture stage is completed and may be used to change stage volume on the fly. Figure 1: Fracture Geometry Model with FDI Pressure Matching Case studies are presented showing the full workflow required to generate the fracture geometry model. The component inputs for the model are presented including a toe-stage DFIT, inter-stage pressure fall-off, and the FDI pressure build-up. We discuss how to optimize these hydraulic fractures in hindsight (look-back) and what might have been done in real time during the completion operations given this workflow and field-ready advanced data-handling capability. Hydraulic fracturing operations can be optimized in real time using new Rapid-PTA techniques for high quality pressure data collected on treating and observation wells. This process opens the door for more advanced geometry modeling and for rapid design changes to save costs and improve well productivity and ultimate recovery.
Johnson, Caroline (Heriot-Watt University–Edinburgh (*Corresponding author) | Sefat, Morteza Haghighat (email: email@example.com)) | Elsheikh, Ahmed H. (Heriot-Watt University–Edinburgh) | Davies, David (Heriot-Watt University–Edinburgh)
Summary In the next decades, tens of thousands of well plugging and abandonment (P&A) operations are expected to be executed worldwide. Decommissioning activities in the North Sea alone are forecasted to require 2,624 wells to be plugged and abandoned during the 10-year period starting from 2019 (Oil&Gas_UK 2019). This increase in decommissioning activity level and the associated high costs of permanent P&A operations require new, fit-for-purpose, P&A design tools and operational technologies to ensure safe and cost-effective decommissioning of hydrocarbon production wells. This paper introduces a novel modeling framework to support risk-based evaluation of well P&A designs using a fluid-flow simulation methodology combined with probabilistic estimation techniques. The developed well-centric modeling framework covers the full range of North Sea P&A well designs and allows for quantification of the long-term (thousands of years) evolution of hydrocarbon movement in the plugged and abandoned well. The framework is complemented by an in-house visualization tool for identification of the dominant hydrocarbon flow-paths. Monte Carlo methods are used to account for uncertainties in the modeling inputs, allowing for robust comparison of various P&A design options, which can be ranked on the basis of hydrocarbon leakage risks. The proposed framework is able to model transient conditions within the well P&A system, allowing for the development of new key performance indicators (e.g., time until hydrocarbons reach surface and changes in hydrocarbon saturation within the P&A well). Such key performance indicators are not commonly used, because most published work in this area relies on steady-state P&A models. The developed framework was used in the assessment of several P&A design cases. The results obtained, which are presented in this paper, demonstrate its value for supporting risk-baseddecision-making by allowing for quantitative comparison of the expected performance of multiple P&A design options for given well/reservoir conditions. The framework can be used for identifying cost-effective, fit-for-purpose P&A designs, for example by optimizing the number, location, and length of wellbore barriers and evaluating the effectiveness of annular cement sheath remedial operations. Additionally, this framework can be used as a sensitivity analysis tool to identify the critical parameters that have the greatest impact on the modeled leakage risks, to guide data acquisition plans and model refinement steps aimed at reducing the uncertainties in key model parameters.
Tsuchihashi, Naoki (University of Tokyo) | Wada, Ryota (University of Tokyo (Corresponding author) | Ozaki, Masahiko (email: firstname.lastname@example.org)) | Inoue, Tomoya (University of Tokyo) | Mopuri, Konda Reddy (Japan Agency for Marine-Earth Science and Technology) | Bilen, Hakan (University of Edinburgh) | Nishiyama, Tazuru (University of Edinburgh) | Fujita, Kazuhiro (Japan Petroleum Exploration Co., Ltd.) | Kusanagi, Kazuya (INPEX Corporation)
Summary A real-time stuck pipe prediction using the deep-learning approach is studied in this paper. Early signs of stuck pipe, hereinafter called stuck, are assumed to show common patterns in the monitored data set, and designing a data clip that well captures these features is critical for efficient prediction. With the valuable input from drilling engineers, we propose a 3D-convolutional neural network (CNN) approach with depth-domain data clip. The clip illustrates depth-domain data in 2D-histogram images with unique abstraction of the time domain. Thirty field well data prepared in multivariate time series are used in this study--20 for training and 10 for validation. The validation data include six stuck incidents, and the 3D-CNN model has successfully detected early signs of stuck in three cases before the occurrence. The portion of the data clip contributing to anomaly detection is indicated by gradient-weighted class activation map (grad-CAM), providing physical explanation of the black box model. We consider such explanation inevitable for the drilling engineers to interpret the signs for rational decision-making. Introduction Stuck is one of the major drilling problems that accounts for nonproductive time. In the past, one-third of the wells drilled in the Gulf of Mexico and the North Sea experience stuck pipe problems (Howard et al. 1994). Moreover, stuck may lead to wellbore abandonment in the worst scenario. Regarding its severity, there is a strong demand for early stuck sign-detection methods to avoid stuck incidents. The drillstring is considered to be stuck when it loses the freedom of movement; that is, it can neither rotate nor move up and down. Also, it cannot be retrieved from the wellbore due to external force. According to the mechanisms, stuck is roughly classified into three types, namely mechanical stuck, differential stuck, and geometrical stuck (Alshaikh et al. 2018). Each stuck type could be separated into smaller classes, for example, mechanical stuck includes both packoff and bridging.
Soares, Ricardo Vasconcellos (NORCE Norwegian Research Centre and University of Bergen (Corresponding author) | Luo, Xiaodong (email: email@example.com)) | Evensen, Geir (NORCE Norwegian Research Centre) | Bhakta, Tuhin (NORCE Norwegian Research Centre and Nansen Environmental and Remote Sensing Center (NERSC))
Summary In applications of ensemble-based history matching, it is common to conduct Kalman gain or covariance localization to mitigate spurious correlations and excessive variability reduction resulting from the use of relatively small ensembles. Another alternative strategy not very well explored in reservoir applications is to apply a local analysis scheme, which consists of defining a smaller group of local model variables and observed data (observations), and perform history matching within each group individually. This work aims to demonstrate the practical advantages of a new local analysis scheme over the Kalman gain localization in a 4D seismic history-matching problem that involves big seismic data sets. In the proposed local analysis scheme, we use a correlation-based adaptive data-selection strategy to choose observations for the update of each group of local model variables. Compared to the Kalman gain localization scheme, the proposed local analysis scheme has an improved capacity in handling big models and big data sets, especially in terms of computer memory required to store relevant matrices involved in ensemble-based history-matching algorithms. In addition, we show that despite the need for a higher computational cost to perform model update per iteration step, the proposed local analysis scheme makes the ensemble-based history-matching algorithm converge faster, rendering the same level of data mismatch values at a faster pace. Meanwhile, with the same numbers of iteration steps, the ensemble-based history-matching algorithm equipped with the proposed local analysis scheme tends to yield better qualities for the estimated reservoir models than that with a Kalman gain localization scheme. As such, the proposed adaptive local analysis scheme has the potential of facilitating wider applications of ensemble-based algorithms to practical large-scale history-matching problems.
Zhang, Kai (China University of Petroleum (Corresponding author) | Wang, Yanzhong (email: firstname.lastname@example.org)) | Li, Guoxin (China University of Petroleum) | Ma, Xiaopeng (PetroChina) | Cui, Shiti (China University of Petroleum) | Luo, Qin (Southwest Petroleum University) | Wang, Jian (Exploration and Development Research Institute of PetroChina Tarim Oilfield Company) | Yang, Yongfei (China University of Petroleum) | Yao, Jun (China University of Petroleum)
Summary We are interested in the development of surrogate models for the prediction of field saturations using a fully convolutional encoder/decoder network based on the dense convolutional network (DenseNet; Huang et al. 2017), similar to the approaches used for image/image-regression tasks in deep learning. In the surrogate model, the encoder network automatically extracts the multiscale features from the raw input data, and the decoder network then uses these data to recover the input image resolution at the output of the model. The input of multiple influencing factors is considered to make our surrogate model more consistent with the physical laws, which has achieved good results in the prediction of output fields in our experiments. Various reservoir parameters including the static reservoir properties (i.e., permeability field) and dynamic reservoir properties (i.e., well placement) are used as input features, and the water-saturation distributions in different periods are taken as the output. Compared with traditional numerical reservoir simulation, which has a high computational cost and is time consuming, not only does it present the same precision, but it costs less time. At the same time, it can also be used for production optimization and history matching.
The first fracture treatments were pumped just to see if a fracture could be created and if sand could be pumped into the fracture. In 1955, Howard and Fast published the first mathematical model that an engineer could use to design a fracture treatment. The Howard and Fast model assumed the fracture width was constant everywhere, allowing the engineer to compute fracture area on the basis of fracture fluid leakoff characteristics of the formation and the fracturing fluid. Modeling of fracture propagation has improved significantly with computing technology and a greater understanding of subsurface data. The Howard and Fast model was a 2D model.
Fracture diagnostic techniques are divided into several groups. Direct far-field methods consists of tiltmeter-fracture-mapping and microseismic-fracture-mapping techniques. These techniques require sophisticated instrumentation embedded in boreholes surrounding the well to be fracture treated. When a hydraulic fracture is created, the expansion of the fracture causes the earth around the fracture to deform. Tiltmeters can be used to measure the deformation and to compute the approximate direction and size of the created fracture.
Resistivity is the one of the most difficult formation parameters to measure accurately because of the complex changes that occur during and after drilling a well and that may still be occurring during logging. The various components of the downhole environment may have strongly contrasting resistivities, some of which cannot be measured directly, and their physical dimensions may not be readily available. Figure 1 shows an idealized relationship of the main environmental components. There is no direct measurement of Rt. It must be inferred from the multiple-depth resistivity measurements.
Yao, Bo (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China) (Corresponding author) | Chen, Jiaqi (email: email@example.com)) | Li, Chuanxian (Petrochina Planning and Engineering Institute, China National Petroleum Corporation) | Yang, Fei (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China)) | Sun, Guangyu (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China)) | Lu, Yingda (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China))
Summary Accurately predicting wax deposits in a crude pipeline through empirical formulas or numerical modeling is unreliable because of the incomplete mechanism and the time-dependent unsteady actual operating conditions. With the help of the data collected by the supervisory control and data acquisition system of pipelines, wax deposit prediction is made possible by developing the time-dependent data mining method. In this article, the data from a typical long-distance crude pipeline in China operating over a 4-year time period was investigated. The inlet temperature prediction was first conducted by developing the long short-term memory (LSTM)-recurrent neural networks (RNNs) model, during which the feature sequencing, overfitting problems, and optimal hyperparameters were fully considered. Because of the time sequence cell, the accuracy of the LSTM-RNN model, as well as the time consumption, is much better than the RNN model when dealing with a great deal of data over a long period of time. Taking the inlet temperature prediction results as input features, the prediction model of average wax deposit thickness was established based on the backpropagation (BP) neural network and optimized by the particle swarm optimization (PSO), chaos particle swarm optimization (CPSO), and adaptive chaos particle swarm optimization (ACPSO) algorithms. The conclusions and associated algorithm from this article help to determine the reasonable pigging circle of longdistance pipelines practically. It could also be applied to guide the wax deposit prediction in the wellbore or oil-gathering pipes. Introduction Wax deposition has always been one of the key issues challenging the safety and economic operation of long-distance crude pipelines (White et al. 2018). Generally speaking, under the impact of flow and temperature fields, paraffin waxes may precipitate and deposit on the pipe wall coupling with the nucleation or cocrystallization of crude components such as asphaltenes or resins to form a layer of sediments called the wax deposit (Wang et al. 2017, 2019b). The wax deposit will turn thicker and stronger as wax precipitates and time passes. The wax deposit will reduce the effective diameter of pipelines, reducing the transportation quantity of crude oil and even blocking pipelines in serious cases (Guozhong and Gang 2010). To alleviate the problems discussed previously caused by thick wax deposition, the most direct and practical way is pigging regularly (Li et al. 2019b).