A Novel Adaptive Non-Linear Regression Method to Predict Shale Oil Well Performance Based on Well Completions and Fracturing Data

Bakshi, Amol; Uniacke, Eric; Korjani, Mohammad M.; Ershaghi, Iraj

doi:10.2118/185695-ms

Cited by 15 publications

(3 citation statements)

References 6 publications

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“…Surge and swab pressure variations are may be negative or positive [31]. positive when dropping the pipe down and negative when retreating the pipe up [32]. The strength of those pressure variations be contingent on the lowering the speed in other words, (tripping in) or retreating of the pipe out in other words, (tripping out).…”

Section: Fig 4 Effect Of the Velocity And Flow Behavior Index [21]mentioning

confidence: 99%

Swab – Surge Pressure Investigation, and the Influence Factors, Prediction and Calculation (Review)

Assi

2023

IJCPE

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Surge pressure is supplemental pressure because of the movement of the pipes downward and the swab pressure is the pressure reduction as a result of the drill string's upward movement. Bottom hole pressure is reduced because of swabbing influence. An Investigation showed that the surge pressure has great importance for the circulation loss problem produced by unstable processes in the management pressure drilling (MPD) actions. Through Trip Margin there is an increase in the hydrostatic pressure of mud that compensates for the reduction of bottom pressure due to stop pumping and/or swabbing effect while pulling the pipe out of the hole. This overview shows suggested mathematical/numerical models for simulating surge pressure problems inside the wellbore with adjustable cross-section parts. The developed models require simple input data that may be gotten from the rig location. Pressure variations due to Swabs and surge has been a major concern in the oil industry for numerous years. If the pressure variations become moreover extraordinary, this leads to formation fracture, and formation influx principal to a kick. In the worst circumstances and situations that kick principal on the blowout and put crew life in hazard. By using theoretical investigation and experimental consequences, it established that the surge pressure is a function of the well depth, the drilling tools combination, the diameter of the wellbore, drilling mud properties, drilling pipe operation speed, and acceleration of the drill pipe movement, etc. This review focuses and investigates the essential theory and on software that computes the pressure variations in different flow conditions to predict surge and swab pressure values.

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Section: Fig 4 Effect Of the Velocity And Flow Behavior Index [21]mentioning

confidence: 99%

Swab – Surge Pressure Investigation, and the Influence Factors, Prediction and Calculation (Review)

Assi

2023

IJCPE

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“…The effective annular mud velocity is given by Equation (43). The mean of the effective annular mud velocity is the mud velocity that creates the surge pressure's viscus drag with reference to the wellbore wall.…”

Section: Hmentioning

confidence: 99%

“…Principal component analysis was utilized by Kormaksson et al [42] to find economically viable sites for new wells. Bakshi [43] developed a unique nonlinear regression model to predict shale oil well performance, including optimal well sites and completion parameters. Temizel et al [44] investigated the factors that impact vertical and horizontal well performance in confined reservoirs.…”

Section: Drillingmentioning

confidence: 99%

Modeling of Swab and Surge Pressures: A Survey

Mohammad

Davidrajuh

2022

Applied Sciences

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Swab and surge pressure fluctuations are decisive during drilling for oil. The axial movement of the pipe in the wellbore causes pressure fluctuations in wellbore fluid; these pressure fluctuations can be either positive or negative, corresponding to the direction of the movement of the pipe. For example, if the drill string is lowering down in the borehole, the drop is positive (surge pressure), and if the drill string is pulling out of the hole, the drop is negative (swab pressure). The intensity of these pressure fluctuations depends on the speed of the lowering down (tripping in) or withdrawing the pipe out (tripping out). High tripping speed corresponds to higher pressure fluctuations and can lead to fracturing the well formation. Low tripping speed leads to a slow operation, causing non-productive time, thus increasing the overall well budget. Researchers used mathematical equations and physics to understand the phenomena and have provided many empirical, mathematical, and physics-based models. This paper starts with a literature study on the swab and surge pressures. After that, this paper concludes with a proposal for a new approach. The new approach proposes developing new models that are more robust, using field data, as we have access to field data from drilling operations. Research using field data would provide data-driven methodologies as new solutions for the rate of penetration, reservoir management, and drilling optimization. The expected outcome will improve the performance of the tripping in and tripping out process within drilling and well construction, and will further reduce the risk related to swab and surge pressures.

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Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Alzahabi

Trindade

Kamel

et al. 2020

J Petrol Explor Prod Technol

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Machine learning techniques have fundamentally altered how oil and gas industry practitioners design fracture operations. In this paper, we perform data analytics utilizing response surface methodology (RSM), a group of statistical techniques that develop a functional relationship between an output variable of interest and several associated input variables, to optimize the output. We apply RSM to optimize horizontal well production based on initial production (IP) of horizontal oil wells for 180 days (IP180 Oil), as a function of five input variables: reservoir type, fracturing fluid (gal/ft), proppants (lbm/ft), cluster spacing, and stage length (ft). The RSM model correlates the initial production of each well to the input variables via a single equation, thus allowing for exploration of the fitted response surface in order to maximize production. Although the choice of the five inputs is made based primarily after consultation with industry professionals, we validate our selection by also applying an assortment of data-analytics-based methods that attempt to rank variable importance and thereby identify completion variables that may be predictive of initial production. The findings rank all five variables above the 50th percentile, thus indicating that the chosen variables have merit. This procedure is applied to a dataset of 201 horizontal wells from the Wolfcamp formations. The model fits reasonably well, with R2 = 61%, a very significant F-statistic p value, and a predicted versus observed values scatterplot indicating a good fit. The RSM analysis suggests that, within the feasible space defined by this dataset, maximum values of IP 180 Oil may be obtained by setting the fracturing fluid in gal/ft at approximately 1972, while simultaneously maximizing the remaining input variables (proppant loading, cluster spacing, stage length). The outcome indicates the possible directions to be taken in seeking a global optimum initial production for the setting of completion variables. Iteration of this scheme may lead to a near-optimum global solution. The real utility of this work may be indicating the way different studies may be designed to optimize production, each with its own selection of inputs, and ultimately be combined in a meta-analysis.

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A Novel Adaptive Non-Linear Regression Method to Predict Shale Oil Well Performance Based on Well Completions and Fracturing Data

Cited by 15 publications

References 6 publications

Swab – Surge Pressure Investigation, and the Influence Factors, Prediction and Calculation (Review)

Swab – Surge Pressure Investigation, and the Influence Factors, Prediction and Calculation (Review)

Modeling of Swab and Surge Pressures: A Survey

Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

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