Latest Petrophysics

Facies interpretation from wireline logs using machine learning (ML) technique.

noreply@blogger.com (Shakeel Ahmad Mughal) — Wed, 16 Jun 2021 16:23:00 +0000

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Reservoir facies determination is the most important job in oil and gas exploration which relies on the major of rock propertied. Fundamental properties of rocks are usually understood by their detailed description in the field (lithofacies analysis) and laboratory (petrofacies analysis). The facies (lithofacies and petrofacies) determination in most subsurface studies is impractical, due to lack of cores and cuttings. In such situations, where the wire line logs are the only data available, the logfacies or electrofacies are determined instead. Electrofacies can be determined by using unsupervised machine learning technique i.e. K-mean cluster analysis.

What is a Cluster?

A cluster is a collection of data objects which are similar (or related) to one another within the same group (i.e., cluster).

What is Cluster Analysis?

The purpose of cluster analysis is to discover important patterns in large datasets (Wagstaff 2012).

Cluster analysis is an unsupervised machine learning algorithm of grouping a large dataset into significant subgroups so that the data points in the same class have the same characteristics and different from another subgroup. It was first introduced by Queen (1966). It has several applications in various fields, for example, data mining (Fayyad et al. 1996), compression of data, and quantization of vector (Gersho and Gray 2012).

Cluster Analysis for rock typing or lithology interpretation?

Similar facies may have different log responses due to diverse factors that affect the logs. Since using statistical methods and procedures are mandatory, in the clustering procedure, data are grouped with a minimum distance and maximum homogeneity.

Distinct geological parameters can be related to a group of data, as log facies, which be used by geologists for further interpretation. For this calculation, all log readings are considered as “observations” and the used logs as the “values of the observations” (Tavakoli and Amini 2006).

K-mean cluster analysis is easy and simple to understand, and it is fast and robust to cluster large dataset. K-mean cluster analysis is a well-known clustering algorithm because of its easy implementation and efficiency (Nazeer and Sabestian 2009). K-mean clustering is an unsupervised learning algorithm, and the main aim of K-mean clustering is to partition n number of observations into K number of clusters. For the numerical dataset, the centre of each cluster is represented by the mean/centroid. In each cluster, every observation belongs to the nearest mean.

Mathematically, K-mean cluster analysis can be written as (Wang et al. 2012);

K-mean cluster analysis works in the following way:

1. We define some clusters (K) randomly.

2. K-mean cluster divides the data points into subsets and allocates the centroids to each subset or cluster.

3. K-mean computes the clusters again and assign the data points to their nearest centroids.

4. The second and third step is iterated again and again until the arithmetic means/centroids do not change any more ( Ali et al., 2020).

The cluster analysis method is used to perform the log facies classification based on the attempts to identify clusters of well logs responses with similar characteristics. This classification does not require any artificial subdivision of the data population but follows naturally based on the unique characteristics of well-log measurements, reflecting minerals and lithofacies within the logged interval (Al-Baldawi 2014).

Example:

Below is an example of crossplots and histograms between gamma ray log, porosity, and water saturation as generated by k-means cluster analysis for a specified reservoir (Image by Al-Baldawi, 2014)

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Interpretation of combination Well Logs (Slide Show)

noreply@blogger.com (Shakeel Ahmad Mughal) — Sat, 25 Oct 2014 20:43:00 +0000

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Home

noreply@blogger.com (Shakeel Ahmad Mughal) — Sat, 25 Oct 2014 18:21:00 +0000

This is all about petrophysics and wireline logging

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This post is being updated..

noreply@blogger.com (Shakeel Ahmad Mughal) — Sat, 25 Oct 2014 18:16:00 +0000

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DIFFERENCE BETWEEN NMR AND CONVENTIONAL LOGS

noreply@blogger.com (Shakeel Ahmed) — Mon, 20 Oct 2014 05:48:00 +0000

Ultrasonic Borehole Imager (UBI)

noreply@blogger.com (Shakeel Ahmed) — Mon, 20 Oct 2014 05:45:00 +0000

Other Common Names to this log:

1. CBIL (pronounced Cybill) stands for Continuous Borehole Image Log

2. Borehole televiewer

COMPARISON WITH OTHER IMAGE LOGS:

Acoustic image logs, also called televiewer or CBIL logs, use a rotating transducer to measure acoustic impedance images over the entire borehole wall, as well as an acoustic caliper.
Resistivity imager logs, also called microscanner or FMS or FMI logs, carry an array of electrodes on pads used to produce an electrical image of the formations seen on the borehole wall. Microscanners have better vertical resolution and dynamic range than televiewers, but televiewers see the entire wellbore while microscanners usually see less than 100%.

Characterization of rock structure From FMI Logs

noreply@blogger.com (Shakeel Ahmed) — Tue, 31 Jan 2012 19:47:00 +0000

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FMI images provide critical information if the rock structure and sedimentary features are significant determinants of formation productivity. The figure below shows paired carbonates and sandstones with the same porosity but completely different permeabilities and hence production capabilities.

FRACTURE IDENTIFICATION FROM FMI LOGS

noreply@blogger.com (Shakeel Ahmed) — Sat, 31 Dec 2011 19:40:00 +0000

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The electrical images are made by applying a gray scale to the resistivity wiggle-traces produced from the electrodes on the tool. In this way, low resistivity zones appear dark and high resistivity, low porosity intervals appear white. Since the array on each pad is two and a half inches wide, irregular features, such as vugs and fractures, show up as dark spots and lines on the images. Colour tones may be used instead of grey.
The image depth scale is usually 1:20 or 1:40, and the X axis is scaled from -180 to +180 degrees around the borehole, putting North in the middle of the track. Examples are provide in the image. A dramatic near vertical fracture can be seen. Two vertical scales are used: one for reconnaissance and one for detail evaluation. Fracture orientation is roughly NNW - SSE dipping at more than 80 degrees. Other images on these two figures illustrate induced fractures, borehole breakout, inter-bedding laminations, slump brecchia, vugs with fractures, and stylolites.

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A video on Neutron-Density interpretation tip

noreply@blogger.com (Shakeel Ahmed) — Sat, 24 Dec 2011 19:35:00 +0000

How to QC FMI data?

noreply@blogger.com (Shakeel Ahmed) — Mon, 19 Dec 2011 17:59:00 +0000

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1. Check the calliper curves and see if the hole shape is
good and has no washouts.

2. Check the FMI images.
Are they readable?
Did all the pads work well?

3. Finally, it is good to check the deviation data of the well
and compare it with the deviation curve presented with the
FMI data.

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NMR FOR FLUID PROPERTIES AND POROSITY

noreply@blogger.com (Shakeel Ahmed) — Wed, 07 Dec 2011 17:26:00 +0000

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NMR LOG'S FOR FORMATION FLUID PROPERTIES

Medical MRI relies on the ability to link specific medical conditions or organs in the body to different NMR behavior. A similar approach can be used with MRIL tools to study fluids in a thin zone a few inches from the borehole wall. MRIL tools can determine the presence and quantities of different fluids (water, oil, and gas), as well as some of the specific properties of the fluids (for example, viscosity). Both Medical-MRI devices and MRIL logging tools can be run with specific pulse-sequence settings, or “activations,” that enhance their ability to detect particular fluid conditions.

NMR AND MICRO POROSITY

Micro-porosity associated with clays and with some other minerals typically contains water that, from an NMR perspective, appears almost like a solid. Water in such micro-pores has a very rapid “relaxation time.” Because of this rapid relaxation, this water is more difficult to see than, for example, producible water associated with larger pores. Earlier generations of NMR logging tools were unable to see water in these micro-pores, and because this water was associated most often with clays, the porosity measured by these earlier tools was often characterized as being an “effective porosity.” Modern MRIL logging tools can see essentially all the fluids in the pore space, and the porosity measurement made by these tools is thus characterized as being a “total-porosity” measurement. Pore-size information supplied by the modern tools is used to calculate an effective porosity that mimics the porosity measured by the older NMR tools.

CALIBRATION OF NMR TO PETROPHYSICAL PROPERTIES IN LABs (AN ADAVANTAGE)

One of the key features of the MRIL design philosophy is that the NMR measurements of the formation made when the MRIL tool is in the wellbore can be duplicated in the laboratory by NMR measurements made on rock cores recovered from the formation. This ability to make repeatable measurements under very different conditions is what makes it possible for researchers to calibrate the NMR measurements to the petrophysical properties of interest (such as pore size) to the end user of MRIL data.

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NMR ( MRIL) Logging Basics

noreply@blogger.com (Shakeel Ahmed) — Tue, 06 Dec 2011 14:29:00 +0000

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Medical MRI and MRI Logging:

Magnetic resonance imaging (MRI) is one of the most valuable clinical diagnostic tools in health care today. With a patient placed in the whole-body compartment of an MRI system, magnetic resonance signals from hydrogen nuclei at specific locations in the body can be detected and used to construct an image of the interior structure of the body. These images may reveal physical abnormalities and thereby aid in the diagnosis of injury and disease. Magnetic Resonance Imaging Logging (MRIL®), introduced by NUMAR in 1991,1 takes the medical MRI or laboratory NMR equipment and turns it inside-out. So, rather than placing the subject at the center of the instrument, the instrument itself is placed, in a wellbore, at the center of the formation to be analyzed.

Comparison of the MRIL Tool to Other Logging Tools

Because only fluids are visible to MRI, the porosity measured by an MRIL tool contains no contribution from the matrix materials and does not need to be calibrated to formation lithology. This response characteristic makes an MRIL tool fundamentally different from conventional logging tools. The conventional neutron, bulk-density, and acoustic-travel-time porosity-logging tools are influenced by all components of a reservoir rock. Because reservoir rocks typically have more rock framework than fluid filled space, these conventional tools tend to be much more sensitive to the matrix materials than to the pore fluids. The conventional resistivity-logging tools, while being extremely sensitive to the fluid-filled space and traditionally being used to estimate the amount of water present in reservoir rocks, cannot be regarded as true fluid-logging devices. These tools are strongly influenced by the presence of conductive minerals and, for the responses of these tools to be properly interpreted, a detailed knowledge of the properties of both the formation and the water in the pore space is required. MRIL tools can provide three types of information, each of which make these tools unique among logging devices:

• information about the quantities of the fluids in the rock

• information about the properties of these fluids

• information about the sizes of the pores that contain these fluids

MRIL/NMR TOOL PRINCIPLE:

At the center of an MRIL tool, a permanent magnet produces a magnetic field that magnetizes formation materials. An antenna surrounding this magnet transmits into the formation precisely timed bursts of radio-frequency energy in the form of an oscillating magnetic field. Between these pulses, the antenna is used to listen for the decaying “echo” signal from those hydrogen protons that are in resonance with the field from the permanent magnet.

The MRIL-Prime tool can be operated at nine separate frequen-
cies. The use of multiplefrequencies allows independent information to
be obtained from multipleconcentric cylinders,thereby improving the
signal-to-noise ratio,enabling faster loggingspeeds, and permitting
different pulse-timingsequences for complexdata acquisition.

Because a linear relationship exists between the proton resonance frequency and the strength of the permanent magnetic field, the frequency of the transmitted and received energy can be tuned to investigate cylindrical regions at different diameters around an MRIL tool. This tuning of an MRI probe to be sensitive to a specific frequency allows MRI instruments to image narrow slices of either a hospital patient or a rock formation. Fig. 1.2 illustrates the “cylinders of investigation” for the MRIL-Prime tool, which was introduced in 1998. The diameter and thickness of each thin cylindrical region are selected by simply specifying the central frequency and bandwidth to which the MRIL transmitter and receiver are tuned. The diameter of the cylinder is temperature-dependent, but typically is approximately 14 to 16 in.

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Formation Micro-Imager Logs (FMI)

noreply@blogger.com (Shakeel Ahmed) — Mon, 05 Dec 2011 17:00:00 +0000

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HISTORY:

In the late 1980’s Schlumberger introduced the concept of borehole electrical images by processing variations of the shallow microresistivity of wellbore walls recorded by modified versions of its Stratigraphic High Resolution Dipmeter Tool™. Called the Formation Micro-Scanner™ (FMS), the tool measured closely spaced arrays of focused shallow resistivity readings that are related to changes in rock composition and texture, structure, and fluid content [Serra, 1989]. Processing the data, in which a range of colors are assigned to the lateral (side-to-side) and vertical variations of the microresistivity along the wellbore, produces an image of the borehole wall.

WHAT DOES FMI MEASURES:

Image logs are resistivity or acoustic devices that measure certain physical properties of the rock at or near the well that can be displayed as images of the wellbore, which can then be interpreted on a computer. Typically rock properties are controlled by factors such as variations in composition, diagenesis, grain size, grain orientation, pore fluid variations, etc. Image logs can provide detailed picture of the wellbore that represent the geological and petrophysical properties of the section being logged.

WORKING PRINCIPLE:

The current generation of tools, called the fullbore Formation Micro Imager™ (FMI), records an array of microresistivity measurements from 192 sensors on eight pads mounted on four orthogonally placed caliper arms. The spacing and position of the pads provides 80% coverage of an eight-inch diameter hole and a resolution of 5 mm. Other oil field wireline service companies have since developed similar high-resolution electrical borehole imaging tools. The FMI yields a continuous, high-resolution electrical image of a borehole (color-coded for resistivity values), and therefore complements whole cores cut in the same well.

CORE AND FMI:

If the FMI-derived image is of sufficient quality and calibrated against the core, it can provide a continuous survey of the formation in places where core is not cut, there was no core recovery, or when a core has beendamaged through handling, transportation, or plugging.

In the figure FMI image of the 60 ft section representing a Core . On the left, a
dynamic FMI image, on the right, a “Core View” simulating a core. The FMI
color scale presents a range of resistivities from conductive (black) to
resistive (white)

FMI ADVANTAGES:

Determine net pay

The FMI fullbore formation micromager gives you microresistivity formation images in water-base mud. This is the preferred approach for determining net pay in laminated sediments of fluvial and turbidite depositional environments.

Visualize sedimentary features to understand structure

Sedimentary features features define important reservoir geometries and petrophysical reservoir parameters. The interpretation of image-derived sedimentary dip data helps you understand sedimentary structures.

Interpret seismic sections

Well-to-well correlation is difficult in deviated wells with sections of steep and varying structural dip. Greatly improve your structural interpretation of seismic sections with high-quality bedding dips to compute accurate logs of true stratigraphic thickness.

Get more data

Geological information from FMI borehole images helps with stochastic modeling of the sand-shale distribution. FMI images define channel heights superbly in amalgamated units. Other variables, such as the channel width and channel sinuosity, can be estimated using geological analogs, based on detailed sedimentological analysis of FMI image data.

Improve well construction plans

Borehole images improve your mechanical earth models, which in turn helps you optimize well plans. Better understanding of borehole stability can save you millions of dollars during field development.

Benefits

Obtain accurate pay estimates
Interpret formations accurately
Improve reservoir descriptions
Make decisions on site
Get data in difficult environments, including deviated and horizontal wells
Save time and money with complete interpretations in one image pass