Wireline logs vs drilling events: Which one to believe in implying subsurface pressure?

As generally known, subsurface pressure can be implied using both wireline logs and drilling events. However, there may be a case where wireline logs and drilling events do not indicate the same subsurface pressure. Data from four vertical wells located in the South Sumatra Basin, Indonesia, were analyzed as a case study. Two wells, Wells A and D, encountered high overpressured zones, confirmed by drilling events and wireline logs data. The two others, Wells B and C, only encountered low overpressured zones, inferred by the relatively low mudweight used during the drilling. However, the wireline logs of Wells B and C show a reversal as Wells A and D. There are two hypotheses to explain the condition in Wells B and C. First, the wireline logs reversal is due to shallow carbonate cementation. Second, Wells B and C were drilled in an unintentional underbalanced condition. The method used includes XRD, SEM, and titration analysis. The results show that the first hypothesis is false, while the second is true. It may be due to some missing information related to drilling events in the final well report of Wells B and C.

Identification of Geo – Hazard Using Pore Pressure Analysis in ‘MAC’ Field, Niger Delta

Identification of geo-hazard zones using pore pressure analysis in ‘MAC’ field was carried out in this research. Suite of wireline logs from four wells and RFT pressure data from two wells were utilized. Lithologic identification was done using gamma ray log. Resistivity log was used to delineate hydrocarbon and non-hydrocarbon formations. Well log correlation helps to see the lateral continuity of the sands. Pore pressure prediction was done using integrated approaches. The general lithology identified is alternation of sand and shale units. The stratigraphy is typical of Agbada Formation. Three reservoirs delineated were laterally correlated. Crossplot of Vp against density (Rho) colour coded with depth revealed that disequilibrium compaction is the main overpressure generating mechanism in the field. Prediction of overpressure by normal compaction trend was generated and plot of interval transit time against depth show that there is normal compaction from 250m to about 1700 m on MAC-01, but at a depth of about 1800m, there was abnormal pressure build up that shows the onset of overpressure. A relatively normal compaction was observed on MAC-02 until a depth of about 2100m where overpressure was suspected. The prediction of formation pore pressure using Eaton’s and Bower’s method to determine the better of the two methods to adopt for pore pressure prediction shows that the pore pressure prediction using Eaton’s method gave a better result similar to the acquired pressure in the field. Hence Eaton’s method appears to be better suited for formation pore pressure estimation in ‘MAC’ field. The validation of the pore pressure analysis results with available acquired pressure data affirmed the confidence in the interpreted results for this study.

New Method for Predicting Casing Wear in Highly Deviated Wells Using Mud Logging Data

Casing wear is an essential and complex phenomenon in oil and gas wells. Research is being conducted to predict this phenomenon. This study was conducted at a well in southwestern Iran. In this paper, first examine the force exerted on the drill string. Next, the contact force between the drill string and the casing is calculated. Finally, the wear volume and the depth of the wear groove are determined. These calculations were performed using MATLAB and Python software. In addition, due to the high accuracy of coding, mud log data was used to make the results more accurate. It has also been shown that increasing RPM increases the depth of wear and attempts to drill a highly deviated wells as a sliding mode. Finally, compared the results and matched them with the wireline logs recorded from the well.

Indirect Estimation of Clastic Reservoir Rock Grain Size from Wireline Logs Using a Supervised Nearest Neighbor Algorithm: Preliminary Results

Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging. Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions. We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models. For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.

Linear and Nonlinear Controls of Wireline Logs on Automated Grain Size Estimation Using Machine Learning Approach

Wireline logs have been utilized to indirectly estimate various reservoir properties, such as porosity, permeability, saturation, cementation factor, and lithology. Attempts have been made to correlate Gamma-ray, density, neutron, spontaneous potential, and resistivity logs with lithology. The current approach to estimate grain size, the traditional core description, is time-consuming, labor-intensive, qualitative, and subjective. An alternative approach is essential given the utility of grain size in petrophysical characterization and identification of depositional environments. This paper proposes to fill the gap by studying the linear and nonlinear influences of wireline logs on reservoir rock grain size. We used the observed influences to develop and optimize respective linear and machine learning models to estimate reservoir rock grain size for a new well or targeted reservoir sections. The linear models comprised logistic regression and linear discriminant analysis while the machine learning method is random forest (RF). We will present the preliminary results comparing the linear and machine learning methods. We used anonymized wireline and archival core description datasets from nine wells in a clastic reservoir. Seven wells were used to train the models and the remaining two to test their classification performance. The grain size-types range from clay to granules. While sedimentologists have used gamma-ray logs to guide grain size qualification, the RF model recommended sonic, neutron, and density logs as having the most significant grain size in the nonlinear domain. The comparative results of the models' performance comparison showed that considering the subjectivity and bias associated with the visual core description approach, the RF model gave up to an 89% correct classification rate. This suggested looking beyond the linear influences of the wireline logs on reservoir rock grain size. The apparent relative stability of the RF model compared to the linear ones also confirms the feasibility of the machine learning approach. This is an acceptable and promising result. Future research will focus on conducting more rigorous quality checks on the grain size data, possibly introduce more heterogeneity, and explore more advanced algorithms. This will help to address the uncertainty in the grain size data more effectively and improve the models performance. The outcome of this study will reduce the limitations in the traditional core description and may eventually reduce the need for extensive core description processes.

Bio-electrosequence interpretation of late cretaceous sediments of the Southern Bornu Basin, Nigeria

Integrated analysis that involves physical sedimentological, standard palynological and electrofacies analyses on ditch cuttings and suite of wireline logs from Gaibu–1 Well, southern Bornu were examined to identify critical sequence elements and construct a bio-sequence stratigraphical framework. Four (4) palynozones consisting of Triorites africaensis, Cretacaeiporites scabratus - Odontochitina costata, Droseridites senonicus and Syncolporites/Milfordia spp Assemblage Zones construed to be Late Cretaceous – younger successions. Nine (9) depositional sequences each with candidate maximum flooding surfaces (375, 900, 1875, 2250, 2600, 3050, 3400, 3800, 4300 m) marked by marker shales with high abundance and diversity of palynomorphs. Thus, equate with the local lithostratigraphy and global large-scale depositional cycles with candidate sequence boundaries (50, 725, 1625, 2175, 2490, 2850, 3300, 3610, 3960, 4470 m) ranging about 96.28 to 70.07 Ma. The delineated transgressive surfaces along the built sequences mark the subjected onset of marine flooding characterised with interchange of progradational to retrogradational facies. Delineated sequence elements generally show up-hole from progradational to retrogradational and aggradational that represents Lowstand Systems Tracts (LSTs), Transgressive Systems Tracts (TSTs) and Highstand Systems Tracts (HSTs) respectively. The LSTs are seen in form of prograding complex and slope fans, suggestive of good reservoirs. The TSTs consist of channel sand units and shales that depict retrogradational marine units, which could serve as both seals and source rocks for the sand units. The HSTs are made up of interplay of aggradational to progradational sediment packages that could serve as a potential source rock. The palaeoenvironmental indices depict the successions are deposited within continental to open marine settings.

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

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.

Petrophysical characterization for Thebes and Mutulla reservoirs in Rabeh East Field, Gulf of Suez Basin, via well logging interpretation

The current work assesses the sandstones of the Mutulla Formation as well as the limestone of the Thebes Formation for being promising new oil reservoirs in Rabeh East field at the southern portion of the Gulf of Suez Basin. This assessment has been achieved through petrophysical evaluation of wireline logs for three wells (RE-8, RE-22 and RE-25). The visual analysis of well logs data revealed that RE-25 Well is the only well demonstrating positive criteria in five zones for being potential oil reservoirs. The favourable zone within Thebes Formation locates between depths 5084 ft and 5100 ft (Zone A). However, the other positive zones in Mutulla Formation occur between depths: 5403.5–5413.5 ft (Zone B), 5425.5–5436 ft (Zone C), 5488–5498 ft (Zone D) and 5558.5–5563.5 ft (Zone E). The quantitative evaluation shows that the Zone A of Thebes Formation is the best oil-bearing zone in RE-25 Well in terms of reservoir quality since it exhibits lowest shale volume (0.07), minimum water saturation (0.23) and lowest bulk volume of water (0.03). These limestone beds include type of secondary porosity beside the existing primary porosity. On the other hand, the sandstones of Mutulla Formation in RE-25 contain four reservoir zones (B, C, D and E) with the total net pay thickness of 35.5 ft. Moreover, the obtained results revealed that it is expected for zones B, C and D to produce oil without water but Zone E will produce oil with water.

Improved well logs clustering algorithm for shale gas identification and formation evaluation

The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.