Percentile-Oqive Approach Determines the Textural Parameters of Xa Field Lithology and the Suitable Technique for Porosity Estimates

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Atat, J., Akpabio, I., & Ekpo, S. (2022). Percentile-Oqive Approach Determines the Textural Parameters of Xa Field Lithology and the Suitable Technique for Porosity Estimates. Current Science, 2(5), 230–240. Retrieved from http://currentscience.info/index.php/cs/article/view/113
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Abstract

Assessment of textural parameters was carried out to decide on a better technique suitable for porosity estimates in the area of study. Data were obtained from wells A and B and used to generate suites of logs like gamma ray, density and sonic. Microsoft Excel was used for the analysis. The lithology was identified as sand for gamma ray information less than 75 API (or shale if this value is greater than 75 API). Three major Techniques (such as Techniques one, two and three) as deliberated in the subsection of the discussion were examined. Others are Techniques four and five for both wells A and B. The average result of porosity estimates for the three major Techniques are approximately 33%, 35% and 20% from one, two and three respectively for well A. also, 28%, 31% and 16% from one, two and three respectively for well B. With the result of semi-interquartile range, Technique three is seen with the lowest range of spread of the result (that is, 2.75 for well A and 3.00 for well B) and is strongly recommended as the best approach for porosity estimates. Where only sonic data is available, Technique one show better result and should be preferred over Technique two. The coefficient of variation shows that all the results obtained from these five approaches fall within low variance. The matrices making up the lithology are therefore, very poorly sorted, near symmetrically skewed and platykurtic for well A; extremely poorly sorted, coarse skewed indicating high energy environment and leptokurtic for well B. Moreso, the porosity information deduced for both wells from Technique three, categorised them into the good class.

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Abstract

Assessment of textural parameters was carried out to decide on a better technique suitable for porosity estimates in the area of study. Data were obtained from wells A and B and used to generate suites of logs like gamma ray, density and sonic. Microsoft Excel was used for the analysis. The lithology was identified as sand for gamma ray information less than 75 API (or shale if this value is greater than 75 API). Three major Techniques (such as Techniques one, two and three) as deliberated in the subsection of the discussion were examined. Others are Techniques four and five for both wells A and B. The average result of porosity estimates for the three major Techniques are approximately 33%, 35% and 20% from one, two and three respectively for well A. also, 28%, 31% and 16% from one, two and three respectively for well B. With the result of semi-interquartile range, Technique three is seen with the lowest range of spread of the result (that is, 2.75 for well A and 3.00 for well B) and is strongly recommended as the best approach for porosity estimates. Where only sonic data is available, Technique one show better result and should be preferred over Technique two. The coefficient of variation shows that all the results obtained from these five approaches fall within low variance. The matrices making up the lithology are therefore, very poorly sorted, near symmetrically skewed and platykurtic for well A; extremely poorly sorted, coarse skewed indicating high energy environment and leptokurtic for well B. Moreso, the porosity information deduced for both wells from Technique three, categorised them into the good class.

Keyword : Skewness, Standard Deviation, Textural Parameters, Median, Porosity

How to Cite
Atat, J., Akpabio, I., & Ekpo, S. (2022). Percentile-Oqive Approach Determines the Textural Parameters of Xa Field Lithology and the Suitable Technique for Porosity Estimates. Current Science, 2(5), 230–240. Retrieved from http://currentscience.info/index.php/cs/article/view/113
  Submitted
Sep 20, 2022
Published
Oct 25, 2022
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