Minimising Down Hole Mud Losses

Rojas, Juan Carlos; Bern, P.A.; Fitzgerald, B.L.; Modi, S.; Bezant, P.N.

doi:10.2118/39398-ms

Cited by 18 publications

(4 citation statements)

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“…Downhole leakage considerable increases drilling cost and downtime [1][2][3], which often leads to serious accidents because leakage management is a tedious process [4]. Furthermore, the reduction in the height of the liquid column in the wellbore is reduced, resulting in a decrease in effective static liquid column pressure at the well's bottom; this may easily lead to an imbalance in the formation of pore pressure, causing overflow and even blowout [5]. Oil companies and universities have spent considerable amount of money on research related to the problem of drilling fluid leakage and plugging and explored various avenues including hole reinforcement [6], plugging materials [7], mud flow law [8][9][10], drilling fluid diffusion pressure [11] and drilling fluid formulas [12,13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

Zhao

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.

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Section: Introductionmentioning

confidence: 99%

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

Zhao

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…It leads to number of issues including loss of costly drilling fluids, wellbore instability, poorer cooling of the drill bit, formation damage, loss of rheology and lack of cuttings transportation to the surface and lowering of hydrostatic pressure in the annulus (Bourgoyne et al, 1991;Rojas et al, 1998;Tare et al, 2001;Bailey et al, 2000;Walker and Black, 1993;Romero et al, 2006). These issues compromise severely the technical performance and the safety of drilling operations.…”

Section: Introductionmentioning

confidence: 99%

CT Scan Study of the Leak-Off of Oil-Based Drilling Fluids into Saturated Media

2013

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Leak-off of oil-based drilling mud into liquid-saturated cores was studied both theoretically and experimentally. First a simple model for the leak-off process was developed extending an earlier analysis of static filtration into unsaturated cores. Then CT scan aided static filtration experiments were performed in dry, brine-saturated and oil-bearing cores, simulating possible reservoir saturation regimes. Formation of external filter cake and internal filtration of solid particles were visualized and leak-off volumes were measured as function of time. At the end of the experiments the formed external filter cake and internal particle deposition were characterized with the aid of an Electron Scanning Microscope. Using drilling fluids containing carbonate particles it was found that leak-off volumes for saturated cores are larger than for unsaturated cores. It was observed further that leak-off volumes increase with particle size, i.e. consistently with a more permeable external filter cake and limited internal filtration. Leak-off volumes decreased when using smaller hematite particles or barite particles having wider particle distribution size. SPE 165193multi-phase flow in the formation remain insufficiently explored. Recently, we reported an analysis of the leak-off of standard oil-based drilling fluid containing barite and gilsonite into dry Bentheimer sandstone cores. The study introduced an innovative method in which CT scans of the core at time intervals revealed simultaneously the build-up of the external filter cake and internal filtration (Hua et al., 2010;van Overveldt et al., 2011). This technique made it possible to investigate leak-off beyond the capabilities of standard HTHP press test with API filter paper (Aston et al., 2002;Hua et al., 2010). The experiments showed among others things that deep-bed filtration occurred in both deposited filter cake and in the porous medium. However, since cores were initially dry it did not become clear to which extend deep-bed filtration contributed to initiation of the external filter deposition. The microstructure of the external and internal filter cake resulting from the infiltration were examined by Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) (Byrne et al., 2000). The objective of this study is to examine both theoretical the leak-off of oil-based drilling fluid into cores either fully saturated with water or containing oil. The study is a detailed extension of our earlier work (van Overveldt et al., 2011) and employs similar techniques mentioned above. Calcium carbonate particles were used instead of barite and no gilsonite was added to the drilling fluid. Effect of particle size and type on the permeability of the external filter cake was investigated by using smaller sized hematite particles instead of barite or carbonate particles in the formulation of the drilling fluid. The paper proceeds with the presentation of the theory (Section 2). Next, details about the experiments (Section 3) and the results and discussed ...

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“…For de ple te d s ands , the be s t way to manage los t c irc ulation is to pre ve nt rathe r than c ure the proble m. This c an be ac hie ve d us ing a c ombination of a ge ome c hanic s and a f luids approac h. A lite rature s urve y indic ate s that s ignif ic ant work had be e n done in this are a [1][2][3][4][5][6][7][8][9][10][11][12][13] . Los t pre ve ntion mate rials ( LPM ) we re de ve lope d to inc re as e f rac ture initiation or f rac ture propagation pre s s ure .…”

Section: Introductionmentioning

confidence: 99%

Preventing Differential Sticking and Mud Losses Drilling through Highly Depleted Sands. Fluids and Geomechanics Approach

Benaissa¹,

Bachelot²,

Ricaud³

et al. 2005

Proceedings of SPE/IADC Drilling Conference

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fax 01 -972-952-9435. AbstractDrilling through de ple te d s ands c an re s ult in a multitude of proble ms s uc h as los t re turns , dif f e re ntial s tic king, dif f ic ult logging and/ or not be ing able to re ac h the targe t de pth. Of te n c uring los t c irc ulation c an be dif f ic ult and c os tly as a re s ult of as s oc iate d nonproduc tive time and e s c alating mud c os ts . Re me die s s uc h as c e me nt plugs , s que e z e s , e xpandable line r and c as ing while drilling c an be c os tly s olutions .

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Minimising Down Hole Mud Losses

Cited by 18 publications

References 0 publications

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

CT Scan Study of the Leak-Off of Oil-Based Drilling Fluids into Saturated Media

Preventing Differential Sticking and Mud Losses Drilling through Highly Depleted Sands. Fluids and Geomechanics Approach

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