2022
DOI: 10.1002/essoar.10512217.1
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Pore Pressure Prediction in Offshore Niger Delta: Implications on Drilling and Reservoir Quality

Abstract: Despite exploration and production success in Niger Delta, several failed wells have been encountered due to overpressures.Hence, it is very essential to understand the spatial distribution of pore pressure and the generating mechanism in order to mitigate the pitfalls that might arise during drilling. This research provides estimates of pore pressure along three offshore wells using the Eaton's transit time method. An accurate normal compaction trend was estimated using the Eaton's exponent (m=3). Our results… Show more

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Cited by 3 publications
(3 citation statements)
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“…RFR is one of the most widely used ensemble supervised ML algorithms that combine multiple decision trees (Figure 8) for performing regression tasks with continuous target variables (Biau, 2012; Breiman, 2001; Pwavodi et al., 2023). Breiman (2001) proposed this ensemble method which independently builds each decision tree and trained on a random subset truer¯()X,Dn=EΘ[]rn()X,normalΘ,Dn $\bar{r}\left(X,{D}_{n}\right)={E}_{{\Theta }}\left[{r}_{n}\left(X,{\Theta },{D}_{n}\right)\right]$ where X = ( x 1 , x 2 , …, x n ), Θ denotes a random subset of input features, D n is the training data set and E Θ denotes expectation with respect to the random parameter; it is introduced by selecting different subsets of features (represented by Θ) and different data subsets (represented by r n ( X , Θ, D n )), r n represents the prediction made by an individual decision tree within a random forest ensemble (Figure 8).…”
Section: Methodsmentioning
confidence: 99%
“…RFR is one of the most widely used ensemble supervised ML algorithms that combine multiple decision trees (Figure 8) for performing regression tasks with continuous target variables (Biau, 2012; Breiman, 2001; Pwavodi et al., 2023). Breiman (2001) proposed this ensemble method which independently builds each decision tree and trained on a random subset truer¯()X,Dn=EΘ[]rn()X,normalΘ,Dn $\bar{r}\left(X,{D}_{n}\right)={E}_{{\Theta }}\left[{r}_{n}\left(X,{\Theta },{D}_{n}\right)\right]$ where X = ( x 1 , x 2 , …, x n ), Θ denotes a random subset of input features, D n is the training data set and E Θ denotes expectation with respect to the random parameter; it is introduced by selecting different subsets of features (represented by Θ) and different data subsets (represented by r n ( X , Θ, D n )), r n represents the prediction made by an individual decision tree within a random forest ensemble (Figure 8).…”
Section: Methodsmentioning
confidence: 99%
“…The Benin Formation is Oligocene and younger. It comprises continental floodplain sands and alluvial deposits with deposits estimated to be 2 km thick [31]. The stratigraphic column of the lithostratigraphic units of the Niger Delta is shown in Fig.…”
Section: Geological Backgroundmentioning
confidence: 99%
“…To assess reserves and forecast fluid flow behaviour, production data analysis entails the interpretation of production rates and pressure data from the reservoir [31], Fig. 5.…”
Section: Production Data Analysismentioning
confidence: 99%