Estimating Formation Tops while Drilling Using Rate of Penetration ROP and Mechanical Specific Energy MSE

2021 ◽  
Author(s):  
Saeed Alshahrani ◽  
Chris Ayadiuno
Keyword(s):  
Specific Energy ◽  
Accurate Determination ◽  
Operational Decisions ◽  
Rate Of Penetration ◽  
Mechanical Specific Energy ◽  
Geological Features ◽  
Weight On Bit ◽  
Wireline Logs ◽  
Key Steps

Abstract Accurate determination of formation tops while drilling is a critical part of exploration geology workflow. Operational decisions on coring, wireline logging, casing, and final well depth largely depend on it. One of the commonly used methods for picking formation tops while drilling is to correlate the rate of penetration (ROP) of the new well to wireline logs from offset wells where there is no logging while drilling (LWD) data. Picking formation tops based on only ROP from a new well can result in picking the wrong formation tops. To improve the workflow and outcome, this paper proposes the combination of ROP and Mechanical Specific Energy (MSE) for estimating formation tops while drilling. MSE is a measure of the energy required to crush or drill through a unit volume of rock. Because MSE is related to rock strength, it can be correlated to changes in lithofacies and formation tops. There are three key steps necessary for utilizing mechanical specific energy to estimate formation tops. First, select the input drilling data relevant to the applicable MSE equation. There are several empirical equations in the literature which can be used for estimating MSE. Input data are ROP, Weight on Bit (WOB), Bit Size (BS), Rotation Per Minute (RPM), and Torque (TORQ) from both the offset wells and the new well. Second, utilize a predetermined empirical equation to estimate MSE. Third, correlate MSE and ROP from the new well to both MSE, ROP, and wireline logs from offset wells (where available) to determine formation tops in the new well. Application of the proposed workflow to two wells show 1) distinct bed boundaries, which agree with formation tops picked using wireline logs; (2) that including MSE increases confidence and reliability of the data and makes it easy to identify the different formation boundaries based on the observed features of both MSE and ROP in the new well; and (3) that MSE variations are sensitive to formation strength, which may indicate rock mechanical changes and formation heterogeneity. This paper presents an alternative method of picking formation tops using MSE and ROP while drilling. The preliminary results based on the two test wells showed over 95% match with those picked using wireline logs of the same new well. As a result, this workflow enhances the ability of geoscientists to correlate subsurface geological features, reduces the uncertainty associated with picking formation tops, casing, and coring depths. Furthermore, it improves the confidence in the result, enhances the quality of operational decisions, and reduces the non-productive time (NPT) and well-cost.

Drilling Dynamics, Mechanical Specific Energy Data Help Drill Record Extended-Reach Well

2021 ◽  
Vol 73 (05) ◽  
pp. 59-60
Author(s):  
Chris Carpenter
Keyword(s):  
Specific Energy ◽  
Response Times ◽  
Oil Field ◽  
Abu Dhabi ◽  
Horizontal Section ◽  
Horizontal Drilling ◽  
Actual Weight ◽  
Mechanical Specific Energy ◽  
Weight On Bit ◽  
Extended Reach Well

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203335, “Using MSE and Downhole Drilling Dynamics in Achieving a Record Extended-Reach Well Offshore Abu Dhabi,” by Nashat Abbas and Jamal Al Nokhatha, ADNOC, and Luis Salgado, Halliburton, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. Complex extended-reach-drilling (ERD) wells often present challenges with regard to geological aspects of data requirement and transmittal, reactive geosteering response times, and accuracy of well placement. Such scenarios may require innovative approaches in Middle East carbonate reservoirs. The objective of the complete paper is to illustrate that, by assessing the details of reservoir geology and key operational markers relevant for best practices, drilling approaches can be customized for each reservoir or scenario. Reservoir Background and Geology The planned reservoir section is a single horizontal of approximately 25,000-ft lateral length at a spacing of 250 m from adjacent injectors. The well was drilled from an artificial island. Field A, a shallow-water oil field, is the second-largest offshore field and the fourth-largest field in the world. Horizontal drilling was introduced in 1989, and an extensive drilling campaign has been implemented since then using steerable drilling technologies. This study is concerned only with wells drilled to develop Reservoir B in Field A, which contributes to the main part of initial oil in place and production. The thick limestone reservoir is subdivided into six porous layers, labeled from shallow to deep as A, B, C, D, E, and F. Each porous layer is separated by thin, low-porosity stylolites. The reservoir sublayer B, consisting of approximately 18-ft-thick calcareous limestones, was selected as the target zone for the 25,420-ft horizontal section. ERD, constructed on artificial islands, began on 2014 with a measured depth (MD)/true vertical depth (TVD) ratio approaching 2.2:1 or 2.4:1. A recent ERD well, Well A, was drilled at the beginning of 2020 with a MD/TVD ratio of 5:1. This value is a clear indication of progressively increasing challenges since the start of the project. Mechanical specific energy (MSE) has long been used to evaluate and enhance the rate of penetration (ROP); however, its use as an optimization tool in ERD wells has not been equally significant. This may have been mostly because of historical use of surface-measured parameters, which do not necessarily indicate the energy required to destroy the rock, particularly in ERD wells. Using optimization tools as part of the bottomhole assembly (BHA) downhole close to the bit provides actual weight-on-bit (WOB) and torque-on-bit (TOB) applied to the drilling bit to destroy the rock and, thus, results in more-representative MSE measurements to optimize drilling parameters and ROP in ERD wells.

Evaluation of Derived Controllable Variables for Predicting Rop Using Artificial Intelligence in Autonomous Downhole Rotary Drilling System

2021 ◽  
Author(s):  
Kingsley Williams Amadi ◽  
Ibiye Iyalla ◽  
Yang Liu ◽  
Mortadha Alsaba ◽  
Durdica Kuten
Keyword(s):  
Real Time ◽  
Specific Energy ◽  
Penetration Rate ◽  
Drilling Process ◽  
Stick Slip ◽  
Conservation Of Energy ◽  
Rate Of Penetration ◽  
Mechanical Specific Energy ◽  
Drilling Parameters ◽  
Drilling System

Abstract Fossil fuel energy dominate the world energy mix and plays a fundamental role in our economy and lifestyle. Drilling of wellbore is the only proven method to extract the hydrocarbon reserves, an operation which is both highly hazardous and capital intensive. To optimize the drilling operations, developing a high fidelity autonomous downhole drilling system that is self-optimizing using real-time drilling parameters and able to precisely predict the optimal rate of penetration is essential. Optimizing the input parameters; surface weight on bit (WOB), and rotary speed (RPM) which in turns improves drilling performance and reduces well delivery cost is not trivial due to the complexity of the non-linear bit-rock interactions and changing formation characteristics. However, application of derived variables shows potential to predict rate of penetration and determine the most influential parameters in a drilling process. In this study the use of derived controllable variables calculated from the drilling inputs parameters were evaluated for potential applicability in predicting penetration rate in autonomous downhole drilling system using the artificial neutral network and compared with predictions of actual input drilling parameters; (WOB, RPM). First, a detailed analysis of actual rock drilling data was performed and applied in understanding the relationship between these derived variables and penetration rate enabling the identification of patterns which predicts the occurrence of phenomena that affects the drilling process. Second, the physical law of conservation of energy using drilling mechanical specific energy (DMSE) defined as energy required to remove a unit volume of rock was applied to measure the efficiency of input energy in the drilling system, in combination with penetration rate per unit revolution and penetration rate per unit weight applied (feed thrust) are used to effective predict optimum penetration rate, enabling an adaptive strategize which optimize drilling rate whilst suppressing stick-slip. The derived controllable variable included mechanical specific energy, depth of cut and feed thrust are calculated from the real- time drilling parameters. Artificial Neutral Networks (ANNs) was used to predict ROP using both input drilling parameters (WOB, RPM) and derived controllable variables (MSE, FET) using same network functionality and model results compared. Results showed that derived controllable variable gave higher prediction accuracy when compared with the model performance assessment criteria commonly used in engineering analysis including the correlation coefficient (R2) and root mean square error (RMSE). The key contribution of this study when compared to the previous researches is that it introduced the concept of derived controllable variables with established relationship with both ROP and stick-slip which has an advantage of optimizing the drilling parameters by predicting optimal penetration rate at reduced stick-slip which is essential in achieving an autonomous drilling system. :

Dynamics of Drilling Systems With an Antistall Tool: Effect on Rate of Penetration and Mechanical Specific Energy

SPE Journal ◽  
2019 ◽  
Vol 24 (05) ◽  
pp. 1982-1996
Author(s):  
Thijs Vromen ◽  
Emmanuel Detournay ◽  
Henk Nijmeijer ◽  
Nathan van de Wouw
Keyword(s):  
Specific Energy ◽  
Delay Differential Equations ◽  
Steady State Response ◽  
Deep Drilling ◽  
Rate Of Penetration ◽  
State Response ◽  
Mechanical Specific Energy ◽  
Downhole Tool ◽  
Simulation Results ◽  
Delay Differential

Summary This paper considers the effect of an antistall tool on the dynamics of deep drilling systems. Field results show that the antistall tool increased the rate of penetration (ROP) of drilling systems when compared with ROP in offset wells drilled without this tool. We developed a model–based approach to investigate the effect of this downhole tool on the ROP and on the mechanical specific energy. Toward this, a drillstring model including the antistall tool was constructed; it describes the coupled axial/torsional dynamics in the form of delay differential equations. Simulation results and a dynamic analysis based on averaging the obtained steady–state response show that an increased drilling efficiency was obtained using the antistall tool, resulting in a higher ROP.

A quantitative approach to inner shell losses

1978 ◽  
Vol 36 (1) ◽  
pp. 526-527 ◽  
Author(s):  
R.D. Leapman ◽  
P. Rez ◽  
D.F. Mayers
Keyword(s):  
Energy Transfer ◽  
Cross Section ◽  
Cross Sections ◽  
Accurate Determination ◽  
Background Intensity ◽  
Core Excitation ◽  
Quantitative Basis ◽  
Cross Section Differential ◽  
Bound Electrons

Microanalysis by EELS has been developing rapidly and though the general form of the spectrum is now understood there is a need to put the technique on a more quantitative basis (1,2). Certain aspects important for microanalysis include: (i) accurate determination of the partial cross sections, σx(α,ΔE) for core excitation when scattering lies inside collection angle a and energy range ΔE above the edge, (ii) behavior of the background intensity due to excitation of less strongly bound electrons, necessary for extrapolation beneath the signal of interest, (iii) departures from the simple hydrogenic K-edge seen in L and M losses, effecting σx and complicating microanalysis. Such problems might be approached empirically but here we describe how computation can elucidate the spectrum shape.The inelastic cross section differential with respect to energy transfer E and momentum transfer q for electrons of energy E0 and velocity v can be written as

Fast calibration of CBED patterns for quantitative analysis

1993 ◽  
Vol 51 ◽  
pp. 674-675
Author(s):  
M.A. Gribelyuk ◽  
M. Rühle
Keyword(s):  
Reciprocal Lattice ◽  
Accurate Determination ◽  
Incident Beam ◽  
Crystal Thickness ◽  
Structure Model ◽  
Beam Direction ◽  
Transmitted Beam ◽  
Reciprocal Vector ◽  
On Line

A new method is suggested for the accurate determination of the incident beam direction K, crystal thickness t and the coordinates of the basic reciprocal lattice vectors V1 and V2 (Fig. 1) of the ZOLZ plans in pixels of the digitized 2-D CBED pattern. For a given structure model and some estimated values Vest and Kest of some point O in the CBED pattern a set of line scans AkBk is chosen so that all the scans are located within CBED disks.The points on line scans AkBk are conjugate to those on A0B0 since they are shifted by the reciprocal vector gk with respect to each other. As many conjugate scans are considered as CBED disks fall into the energy filtered region of the experimental pattern. Electron intensities of the transmitted beam I0 and diffracted beams Igk for all points on conjugate scans are found as a function of crystal thickness t on the basis of the full dynamical calculation.

Computer-Assisted High-Re Solution TEM

1990 ◽  
Vol 48 (1) ◽  
pp. 162-163
Author(s):  
F.A. Ponce ◽  
H. Hikashi
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Determination ◽  
Computer Software ◽  
Video Camera ◽  
Image Contrast ◽  
Objective Lens ◽  
Computer Assisted ◽  
Optical Parameters ◽  
Astigmatism Correction

The determination of the atomic positions from HRTEM micrographs is only possible if the optical parameters are known to a certain accuracy, and reliable through-focus series are available to match the experimental images with calculated images of possible atomic models. The main limitation in interpreting images at the atomic level is the knowledge of the optical parameters such as beam alignment, astigmatism correction and defocus value. Under ordinary conditions, the uncertainty in these values is sufficiently large to prevent the accurate determination of the atomic positions. Therefore, in order to achieve the resolution power of the microscope (under 0.2nm) it is necessary to take extraordinary measures. The use of on line computers has been proposed [e.g.: 2-5] and used with certain amount of success.We have built a system that can perform operations in the range of one frame stored and analyzed per second. A schematic diagram of the system is shown in figure 1. A JEOL 4000EX microscope equipped with an external computer interface is directly linked to a SUN-3 computer. All electrical parameters in the microscope can be changed via this interface by the use of a set of commands. The image is received from a video camera. A commercial image processor improves the signal-to-noise ratio by recursively averaging with a time constant, usually set at 0.25 sec. The computer software is based on a multi-window system and is entirely mouse-driven. All operations can be performed by clicking the mouse on the appropiate windows and buttons. This capability leads to extreme friendliness, ease of operation, and high operator speeds. Image analysis can be done in various ways. Here, we have measured the image contrast and used it to optimize certain parameters. The system is designed to have instant access to: (a) x- and y- alignment coils, (b) x- and y- astigmatism correction coils, and (c) objective lens current. The algorithm is shown in figure 2. Figure 3 shows an example taken from a thin CdTe crystal. The image contrast is displayed for changing objective lens current (defocus value). The display is calibrated in angstroms. Images are stored on the disk and are accessible by clicking the data points in the graph. Some of the frame-store images are displayed in Fig. 4.

Sign in / Sign up

Export Citation Format

Share Document