At times, petrophysicists are expected to evaluate potential of the well in time-constraint situations while maintaining consistency of the parameters and interpretation. Other than that, some challenges may also occur when working with older wells where the dataset are not as complete as current wells and processing parameters are not transferable. In this case study, class-based machine learning (CBML) approach is used to perform petrophysical evaluation to identify potential hydrocarbon zones in the target wells. The objective is to find solution to improve efficiency and consistency in those challenging situations. A class-based machine learning (CBML) workflow uses cross-entropy clustering (CEC)-Gaussian mixture model (GMM)- hidden Markov model (HMM) workflow that identifies locally stationary zones sharing similar statistical properties in logs, and then propagates zonation information from training wells to other wells (Jain, et al., 2019). The workflow is divided into two (2) main steps: training and prediction. Key wells which best represent the formation in the field are used to train the model. This approach automatically generates the number of cluster (class) using unsupervised or supervised depending on the input data. The model from key wells data is then used to reconstruct inputs and outputs along with uncertainty and outlier flags. This allows expert to QC and validate the generated class which is the most crucial part of the workflow. Once the model from the key wells has been built, it is applied to predict the same set of zones in the new wells that require interpretation and predict output curves. The result matched well over the good data interval with the petrophysical interpretation result from conventional approach. While in the bad interval, some discrepancies can be observed. The discrepancy was identified easily from the uncertainty and outlier flags which helps petrophysicists to identify which interval to fix or re-evaluate. Some requirements to condition the input were observed (no missing value over the input and outlier) to get the best result. A number of inputs used in the model need to be consistent over the set of wells used in the training and prediction target. This machine learning workflow speeds-up the petrophysical analysis process, reduce analyst bias and improve consistency result between one well to another within the same field. This machine learning application can also generate auto log QC, zonation class for rock typing also reconstructed logs which enrich the petrophysical interpretation even for wells with limited logs availability. This paper offers practical examples and lessons learned of CBML approach application to perform petrophysical evaluation and identify potential zones while being in time-constrained and limited resource situations.
DOI: 10.17014/ijog.v7i3.142The carbonate sequence overlies conformably the tuffaceous sandstone unit, and in turn is conformably underlain by the tuff-sandstone unit, both of which are members of the Citarate Formation. The Citarate carbonate rocks were deposited in an open platform back reef environment, which was temporarily drowned by local sea level rise. Regional Middle Miocene deformation formed NNE-WSW trend faults and E-W trend folds in the researched area. This paper discusses the nature of diagenetic alteration of the Citarate carbonate rocks based on petrographic analyses of twenty surface samples. Carbonate rocks from bottom to top comprise algae packstone, packstone-grainstone, coral-algae packstone, and foraminifer wackestone-packstone. Fragments of coral, coralline red algae, and large foraminifera are the dominant bioclasts in most of the observed samples, whereas echinoids and bivalves are less abundant; they are set in a recrystallized micrite matrix. Planktonic foraminifera are abundant only in few samples. Fragments of plagioclase, igneous volcanic rocks, pyroclastic rocks (tuff), and much less abundant quartz are commonly present in all the studied samples. A generalized diagenesis includes early marine cementation by fibrous aragonite, compaction, aragonite dissolution and/or neomorphism, precipitation of equant-grained calcite cement in a phreatic environment, dissolution to form moldic porosities, dolomitization, the formation of stylolites and fractures, and precipitation of late ferroan calcite during burial. Multiple carbonate cements occur as pore-filling phases, with ferroan calcite cementation taking place during later-stage burial. Secondary porosities were formed during different stages in diagenetic processes, such as dissolution, dolomitization, and stylolite and fracture formations. Although precipitation of nonferroan and ferroan calcite cement occluded porosities, porosity enhancement during early selective dolomitization might still be significant. Current observations also revealed the presence of intraparticle, micro-vuggy, and fracture porosities in different samples.
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