With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the "what if" questions that are raised by the reservoir/completion management team.
Based on historical production data, decline curve analysis (DCA) can be used to monitor production, identify potential problems, and predict well performance, life, and economics. Optimized production history matching is crucial to economic analysis on future operations and decision making. The widely used decline models such as Arps rate-time relations and their variations are based on fitting predefined equations and often times do not work for shale gas and oil wells since most of the production data from these wells exhibit fracture-dominated flow regimes and rarely reach late-time-flow regimes. This approach can mislead the trend of the decline curves and produce poor matches and unreliable production forecasts. A suitable data-driven model combining physical or operating parameters can be greatly beneficial and serve as the basis for decline analysis and prediction.
This paper discusses a method for automatic history matching and decline analysis for shale production data based on machine learning, which can be effectively applied to production surveillance and process automation. This approach is based on time series (TS) analysis and neural networks (NNs), which was then extended to applications with operational parameters available, such as bottom-hole pressure (BHP). The proposed TS and NN models were applied to production data from a Barnett gas well. The historical production data was divided into two parts. The first part of the data is used to train the NN model, and the second part of the data is used to verify the accuracy of the prediction results from each input parameter. The results were analyzed and compared with classic Arps decline models.
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 25699, “The Contribution From an Operator’s Global Subsea-Hardware- Standardization Program to 10,000-psi Subsea Hardware in Parque das Conchas (BC-10),” by Thiago Palmeira Freire de Carvalho and Luiz Olijnik, Shell, and Alan Labes, FMC Technologies, prepared for the 2015 Offshore Technology Conference, Houston, 4–7 May. The paper has not been peer reviewed.
The Parque das Conchas field in the Campos basin offshore Brazil is a phased ultradeepwater heavyoil development tied to the Espírito Santos floating, production, storage, and offloading (FPSO) vessel, moored at a water depth of 1780 m (Fig. 1). This paper addresses the standardized features applied to the 10,000-psi-rated subsea-system components at BC-10.
The BC-10 development is located 120 km offshore Brazil. Comprising several low-pressure reservoirs with medium- to high-viscosity oil, and in depths to 2000 m, the field’s development production was enabled by the use of subsea-boosting-and-separation caisson electrical submersible pumps (ESPs), each powered up to 1,500 hp through a high-voltage three-phase alternating-current transmission system, with pressure boosting of up to 2,300 psi from each pump. The development was divided into three distinct phases that tied five different reservoir structures to the host by use of subsea-tieback field architecture. The three phases are detailed in the complete paper.
Eisses, Amy (University of Nevada) | Kell, Annie (University of Nevada) | Kent, Graham (University of Nevada) | Louie, John (University of Nevada) | Karlin, Robert (University of Nevada) | Driscoll, Neal (University of California) | Baskin, Rob (Optim Seismic Data Solitions) | Pullammanappallil, Satish (Optim Seismic Data Solitions)
Repeating flips in structural polarity within the basin and segmentation of the East Pyramid Lake fault (referred to as the Lake Range fault in this paper) supports the notion that the Pyramid Lake basin provides a natural laboratory to study the details of transtensional deformation. Recent sediments within several large lakes in this region express very well this complex structural transition. In June 2010, the University of Nevada, Reno; Scripps Institution of Oceanography; and the United States Geological Survey, Salt Lake City collected high-resolution (sub-meter) seismic CHIRP data in Pyramid Lake, Nevada. Faults were beautifully expressed in the images that allowed correlation Pyramid Lake Geological Setting The northern Walker Lane is a kinematically linked system of northwest striking, left-stepping, dextral faults; north-striking normal faults; and east-northeaststriking sinistral faults (Faulds et al., 2005). This fault has ruptured in four major earthquakes in the Holocene, which inferred right-lateral shear slip rates of 2.6 0.3 mm/yr (Briggs and Wesnousky, 2004.)
For the 1-dimensional (1D) prediction of the mud-1) pore pressure (PP), overburden gradient (OBG), and weight windoww (MWW), the following input is required: effective stresss ratio and/or Poisson's ratio; 2) cohesive strength (CS), friction angle, (FA) and/or uniaxial compression strength (UCS), and tectonic factor. The first part of the input data is required for the prediction of the upper bound of the MWW, which is the so-called fracture gradient (FG); the second part of input data is used for the prediction of the lower bound of MWW, which is shear failure gradient (SFG). Among these parameters, the effective stress ratio is used in the calculation of minimum horizontal stress (also termed as FG), and the tectonic factor is used in the calculation of maximum horizontal stress. Poisson's ratio is an alternative for the input of the effective stress ratio. In conventional 1D analytical software, such as the Drillworks application , the effective stress ratio can be calculated in terms of Poisson's ratio at a point within the formation along the wellbore trajectory.
Frary, Roxanna N. (University of Nevada) | Louie, John N. (University of Nevada) | Stephenson, William J. (United States Geological Survey ) | Odum, Jackson K. (United States Geological Survey ) | Kell, Annie (University of Nevada) | Eisses, Amy (University of Nevada) | Kent, Graham M. (University of Nevada) | Driscoll, Neal W. (University of California) | Karlin, Robert (University of Nevada) | Baskink, Robert L. (United States Geological Survey) | Pullammanappallil, Satish (Optim) | Liberty, Lee M. (Boise State University)