Hydrocarbon prospectivity of the miocene-pliocene clastic reservoirs, Northern Taranaki basin, New Zealand: integration of petrographic and geophysical studies

In this study, it is aimed to characterize the Early pliocene sandstone (EP-SD) and the Late Miocene-Early Pliocene Mangaa sandstone reservoirs and the efficiency of their sealing cap rocks using the petrographical and petrophysical data of these sandstone zones in northern Taranaki basin, New Zealand. The prospective potential reservoirs were studied using impregnated thin sections, XRD data analysis, and well log data (self-potential, gamma-ray, sonic, density, neutron, shallow\deep resistivity and PEF) to characterize the reservoir zones, in addition to Mercury intrusion capillary pressure data (MICP) to check the efficiency of some potential seals. The EP-SD and the Mangaa sandstone units are typically poorly consolidated very fine sandstone to siltstone, with porosities averaging 25%. The sands are composed of quartz (38.3–57.4%), with common feldspars (9.9–15.2% plagioclase, and 2.7–6.3% K-feldspars) and up to 31.8% mica. In Albacore-1 well to the north of the Taranaki Basin, the Mangaa formation includes three separate for each of the EP-SD zones (EP-SD1, EP-SD2, and EP-SD3), and the Mangaa sequence (Mangaa-0, Mangaa-1, and Mangaa-2). The thin section studies indicate that, the studied samples are grouped into greywackes, arenites and siltstone microfacies with much lithic fragments and feldspars, sometimes with glauconite pellets. From the XRD data, it is achieved that the mineral composition is dominated by quartz, mica/illite, feldspars, and chlorite. The petrophysical investigation revealed absence of pay zones in the EP-SD zones, and presence of thin pay zone with net thickness 5.79 m and hydrocarbon saturation of about 25.6%. The effective porosities vary between 23.6 and 27.7%, while the shale volume lies between 12.3 and 16.9%. Although the shale content is relatively low, the relatively high API (50–112 API of average 75 API) is contributed by the relatively high K-feldspar content and intercalations with thin siltstone and muddy siltstone beds. Sealing units include the intra-formational seals within the Mangaa sequence, mudstones and fine grained units overlying the Mangaa and further intra-formational mudstones, within the shallower EP-SD units. The efficiency of these seals indicates the capability to trap 16.4–40.6 m gas or 17.4–43.0 m oil which is relatively low in correlation with their efficiency in the central parts of the Taranaki Basin Overlying the primary seals, mudstones of the Giant Foresets Formation provide additional regional seal.

A review of basic well log interpretation techniques in highly deviated wells

Drilling deviated wells has become customary in recent times. This work condenses various highly deviated and horizontal well log interpretation techniques supported by field examples. Compared to that in vertical wells, log interpretation in highly deviated wells is complex because the readings are affected not only by the host bed but also the adjacent beds and additional wellbore-related issues. However, understanding the potential pitfalls and combining information from multiple logs can address some of the challenges. For example, a non-azimuthally focused gamma ray logging while drilling (LWD) tool, used in combination with azimuthally focused density and neutron porosity tools, can accurately tell if an adjacent approaching bed is overlying or underlying. Moreover, resistivity logs in horizontal wells are effective in detecting the presence of adjacent beds. Although the horns associated with resistivity measurements in highly deviated wells are unwanted, their sizes can provide important clues about the angle of the borehole with respect to the intersecting beds. Inversion of horizontal/deviated well logs can also help determine true formation resistivities. Additionally, observed disagreement between resistivity readings with nuclear magnetic resonance (NMR) T2 hydrocarbon peaks can indicate the presence or absence of hydrocarbons. Furthermore, variations in pulsed neutron capture cross sections along horizontal wells, measured while injecting various fluids, can indicate high porosity/permeability unperforated productive zones. Finally, great advances have been made in the direction of the bed geometry determination and geologic modeling using the mentioned deviated well logs. More attention is required toward quantitative log interpretation in horizontal/high angle wells for determining the amount of hydrocarbons in place.

Evaluation of Mud Weight Using Safe Mud Window Concept Based on Well Log Data: A Case Study of Well OP-002 in the North Sumatra Basin Area, Indonesia

In the drilling operation of well OP-002 which is located in the North Sumatra Basin at a depth interval of 2887 - 3186 m occurred partial loss, and caving at a depth interval of 500 - 1650 m, where the drilling problem is caused by the use of inappropriate mud weight. Safe mud window analysis is carried out by processing well log data to build PPFG (Pore Pressure Fracture Gradient) and 1D Geomechanics model using several calculation methods. Furthermore, the results of the calculation of pore pressure and fracture gradient are validated with well test data from the well OP-002, so the safe mud window can be determined, and can be used as a basis in the analysis of the drilling problems that occur. The optimum mud weight can minimize wellbore instability, with a limit value that must be greater than the collapse pressure, but not exceeding the minimum insitu stress limit. From the results of the mud safe window analysis, it can be concluded that at a depth interval of 500 - 1650 m caving occurs, because the density value used is smaller than the shear failure gradient, and at a depth interval of 1619 - 2829 m, the density value used is greater than Shmin. To overcome this problem, a mud wight with a safe mud window concept is recommended, namely the selection of the optimum mud weight to be used must be greater than the pore pressure and shear failure gradient and does not exceed the minimum horizontal stress and fracture gradient values.

Supplemental Material: Narrow is normal: Exploring the extent and significance of flooded marine shelves in icehouse, transitional, and greenhouse climate settings

Tabulated data and calculations for the nine clinoform margin systems included in the study, and further explanation of the method used to determine and measure marine topset widths from well-log and outcrop data.<br>

Supplemental Material: Narrow is normal: Exploring the extent and significance of flooded marine shelves in icehouse, transitional, and greenhouse climate settings

Tabulated data and calculations for the nine clinoform margin systems included in the study, and further explanation of the method used to determine and measure marine topset widths from well-log and outcrop data.<br>

Comparisons of Evaluating Fractured Tight Sandstone Reservoirs Pore Structures Based on Borehole Electrical Image and Nuclear Magnetic Resonance NMR Logs

Tight sandstone reservoirs characterization and evaluation is very difficult based on conventional well log data owing to the extremely low porosity and permeability, and strong heterogeneity. The main accumulation spaces of conventional reservoirs are intergranular pores, and the pore size is the main controlling factor of permeability. However, besides intergranular pores, fractures play much greater important role in accumulating hydrocarbon, improving the pore connectivity and pore structure in tight sandstone reservoirs. Hence, it should be accurately predicted the pore structure dredged by fractures to improve the characterization of tight sandstone reservoirs. Generally, nuclear magnetic resonance (NMR) logging is an effective method to evaluate formation pore structure. However, it cannot be well used in fractured reservoirs because the NMR T2 spectra has no any response for fractures with width &lt;2mm. The borehole electrical image log is usable in characterizing fractured reservoirs. The pore spectrum, which is extracted from the borehole electrical image log, can be used to qualitatively reflect the pore size. Hence, it will play an important role in fractured reservoirs pore structure characterization. In this study, based on the comprehensive analysis of the pore spectra, the corresponding mercury injection capillary pressure (MICP) data and pore-throat radius distributions acquired from core samples, a relationship that connects the 1/POR and capillary pressure (Pc) is proposed. Established a model based on formation classification to transform porosity spectrum into pseudo capillary pressure curve. In addition, a Swanson parameter-based permeability prediction model is also developed to extract fractured formation permeability. Meanwhile, to verify the superiority and otherness of borehole electrical image and NMR log, the model that evaluated reservoirs pore structure from NMR log is also established. Based on the application of the proposed method and models in actual formations, the evaluated pore structure parameters and permeabilities from two types of well log data are compared. The results illustrates that in formations with relative good pore structure, the predicted pore structure parameters and permeabilities from these two types of well log data agree well with the drill stem testing data and core-derived result. However, in low permeability sandstones with relatively poor pore structure, the porosity spectra can be well used to evaluate the pore structure, whereas the characterized pore structure from NMR log is overestimated. With the comprehensive research of reservoirs pore structure and permeability, the fractured tight sandstone formations with development value are precisely identified. This proposed method has greatest advantages that the pore structure of fractured reservoirs can be characterized, and the contribution of fractures to the pore connectivity and permeability can be quantified. it is usable in tight sandstone reservoirs validity prediction.

Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs

Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner. In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach. We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.