|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
ABSTRACT The exploratory projects of hydrocarbons in the Parnaíba Basin have primarily targeted Poti and Cabeças Formations. With the rich geological knowledge obtained from the drilling of wells, the Longá Formation is viewed as a potential new exploratory play. This formation, which some studies reckon that can act as seal or source-rock, is characterized by the intercalation of shales, siltstones, and sandstones. During the drilling of a well, with the subsequent detection of gas, three 18 m long whole-cores were extracted for geological and petrophysical studies. In addition, a complete set of conventional and nuclear magnetic resonance (NMR) logs were obtained along with laboratory analyses of routine core analysis (RCA), capillary pressure, NMR, X-ray diffraction (XRD), and rock mechanics, for a complete petrophysical evaluation. The Longá reservoir is a complex reservoir with millimeter-thick laminations and reservoir layers with conductive minerals that suppress the resistivity curve. As a result, the log data had to be integrated with core data and ultimately a Domain-Transfer analysis model in uncored wells to correctly estimate petrophysical properties and make development decisions. The integration of core-log data made it possible to obtain important information about the depositional environment, lithology, reservoir characterization, calibration of the main petrophysical parameters, and mechanical properties of rocks , which can help realize hydraulic fracturing, thereby contributing to production optimization and risk reduction in exploratory projects. The productivity of the well increased by approximately 500% after stimulation of reservoir. Furthermore, the subsequent drilling of a few more exploratory wells revealed the first commercial field of the Longá Formation in the Parnaíba Basin. INTRODUCTION Petrophysical evaluation of thinly laminated reservoirs presents great complexity, especially regarding the estimation of hydrocarbon volume in place. Conventional well logging tools have a vertical resolution, which is larger than the size of the laminations in thinly laminated reservoirs, and thus fail to solve the petrophysical properties of these small layers. In addition, the presence of clay minerals generates an excess conductivity that affects the resistivity curve. The above- mentioned effects are known well in the petrophysical technical literature as complicating factors for the generation of reliable models. Additionally, this case study presents a greater difficulty due to the presence of complex mineralogy that contains metallic, heavy, and conductive minerals, thereby corroborating the need for complementary studies on core-log integration as a way of calibration of the main petrophysical parameters. Geology is strongly related to the in situ measurements performed by well logs, and thus helps in deeply understanding the spatial distribution of petrophysical properties and geometry of the different lithologies. Given the type of reservoir that this work presents, special emphasis will be given to understanding the relationship between geology and petrophysics.
ABSTRACT Facies classification is a crucial task which can improve the chances of success of a well significantly. The relevant classification algorithms take well logs as inputs and classify the formation into distinctive clusters or electrofacies. Integrating the electrofacies with core measurements can lead to an understanding of the geological facies. We develop a general-purpose workflow for unsupervised electrofacies classification, which takes well logs as inputs and can be used for different application scenarios. The clustering is performed using the Gaussian mixture model approach. The optimal number of clusters is automatically determined ensuring repeatable clustering results from multiple realizations of the classification workflow. The workflow was applied on field data from off-shore Norway. We observe high similarity in the resulting facies with the ones determined visually by the field geologist from core data, by comparing their permeability-porosity relationships. This new approach removes the user intervention in the workflow and provides a robust solution for automating the electrofacies classification processing. INTRODUCTION Facies classification is a key element in the evaluation of petrophysical formations and in reservoir characterization. Electrofacies are defined as clusters of similar log responses in a well or a set of wells and their combination with core measurements can lead to geological facies, which can represent series of petrophysical properties. There has been significant progress towards developing automated workflows for facies classification (Busch et al, 1987; Lim et al. 1997; Rabaute, 1998; Qi and Carr, 2005; Skalinski et al., 2006; Tang et al., 2011). There are three main challenges in electrofacies classification. First, the fact that most of the times there are no labeled data necessitates to use an unsupervised classification method. There are various unsupervised learning algorithms like the k-means (Lloyd, 1982) or the hierarchical clustering algorithm (Ward, 1963) to perform classification. However, these algorithms perform "hard" assignment of data points to clusters, in which each data point is associated uniquely with one cluster (Bishop, 2006) and they do not consider the fact that field data can have some uncertainty over the clusters they are assigned. Second, the optimal number of clusters is usually unknown and thus is required to be an input given by the user. Various approaches have been developed to avoid the user’s subjectivity in the choice of the optimal number of clusters and automate the process. Some of the most common ones are the Bayesian Information Criterion (Schwarz, 1978) and the Cross-Entropy Clustering (Tabor and Spurek, 2014), which however do not provide a universally robust solution. Finally, it is very common different realizations of the classification algorithm to give different clustering results, even if the input logs and the algorithm parameters are kept the same for all realizations. This is because each of the input parameters of the algorithm is initialized randomly for each realization and as a result the algorithm converges to a different value of loglikelihood and the clustering is different each time consequently.
Abstract The Río Neuquén field is located thirteen miles north west of Neuquén city, between Neuquén and Río Negro provinces, Argentina. Historically it has been a conventional oil producer, but some years ago it was converted to a tight gas producer targeting deeper reservoirs. The targeted geological formations are Lajas, which is already a known tight gas producer in the Neuquén basin, and the less known overlaying Punta Rosada formation, which is the main objective of the current work. Punta Rosada presents a diverse lithology, including shaly intervals separating multiple stacked reservoirs that grade from fine-grained sandstones to conglomerates. The reservoir pressure can change from the normal hydrostatic gradient to up to 50% of overpressure, there is little evidence of movable water. The key well in this study has a comprehensive set of open hole logs, including NMR and pulsed-neutron spectroscopy data, and it is supported by a full core study over a 597ft section in Punta Rosada. Additionally, data from several offset wells were used, containing sidewall cores and complete sets of electrical logs. This allowed to develop rock-calibrated mineral models, adjusting the clay volume with X-ray diffraction data, porosity and permeability with confined core measurements, and link the logs interpretation to dominant pore throat radius models from MICP Purcell tests at 60,000 psi. Several water saturation models were tested attempting to adjust the irreducible water saturation with NMR and Purcell tests at reservoir conditions. As a result, three hydraulic units were defined and characterized, identifying a strong correlation with lithofacies observed in cores and image logs. A cluster analysis model allowed the propagation of the facies to the rest of the wells (50). Finally, lithofacies were distributed in a full-field 3D model, guided by an elastic seismic inversion. In the main key well, in addition to the open hole logs and core data, a cased hole pulsed neutron log (PNL) was also acquired , which was used to develop algorithms to generate synthetic pseudo open hole logs such as bulk density and resistivity, integrated with the spectroscopy mineralogical information and other PNL data to perform the petrophysical evaluation. This enables the option to evaluate wells in contingency situations where open hole logs are not possible or are too risky, and also in planned situations to replace the open hole data in infill wells, saving considerable drilling rig time to reduce costs during this field development phase. Additionally, the calibrated cased hole model can be used in old wells already drilled and cased in the Punta Rosada formation. This paper explores the integration of different core and log measurements and explains the development of rock-calibrated petrophysical and rock types models for open and cased hole logs addressing the characterization challenges found in tight gas sand reservoirs. The results of this study will be crucial to optimize the development of a new producing horizon in a mature field.
Abstract Lithological facies classification using well logs is essential in the reservoir characterization. The facies are manually classified from characteristic log responses derived, which is challenging and time consuming for geologically complex reservoirs due to high variation of log responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the Yurihara oil field is petrologically classified into five lithofacies: mudstone, hyaloclastite, pillow lava, sheet lava, and dolerite, with pillow lava being predominant reservoir. The former four lithofacies are the members of the volcanic system in Miocene, and dolerite randomly intruded later into those. Understanding the distribution of omnidirectional tight dykes at the well location is important for the estimation of potential near-lateral seal distribution compartmentalizing the reservoir. The facies are best classified by core data, which are unfortunately available in a limited number of wells. The conventional logs, with the help of the borehole image log, have been used for the facies classification in most of the wells. However, distinguishing dolerite from sheet lava by manual classification is very ambiguous, as they appear similar in these logs. Therefore, automated clustering of well logs with ML was attempted for the facies classification. All the available log data was audited in the target well prior to applying ML. A total of 10 well logs are available in the reservoir depth interval. To prioritize the logs for the clustering, the information of each log was first analyzed by Principal Component Analysis (PCA). The dimension of variable space was reduced from 10 to 5 using PCA. Final set of 5 variables, gamma-ray, density, formation photoelectric factor, neutron porosity, and laterolog resistivity, were used for the next clustering process. ML was applied to the selected 5 logs for automated clustering. Cross-Entropy Clustering (CEC) was first initialized using k-means++ algorithm. Multiple initialization processes were randomly conducted to find the global minimum of cost function, which automatically derived the optimized number of classes. The resulting classes were further refined by the Gaussian Mixture Model (GMM) and subsequently by the Hidden Markov Model (HMM), which takes the serial dependency of the classes between successive depths into account. Resulting 14 classes were manually merged into 5 classes referring to the lithofacies defined by the borehole image log analysis. The difference of the log responses between basaltic sheet lava and dolerite was too subtle to be captured with confidence by the conventional manual workflow, while the ML technique could successfully capture it. The result was verified by the petrological analyses on sidewall cores (SWCs) and cuttings. In this study, the automated clustering with the combination of several ML algorithms was demonstrated more efficient and reasonable facies classification. The unsupervised learning approach would provide supportive information to reveal the regional facies distribution when it is applied in the other wells, and to comprehend the dynamic behavior of the fluids in the reservoir.
Abstract The Cretaceous Cape Vulture prospect (Norwegian Sea, Norway) consisted of three Cretaceous sand levels: Cape Vulture Lower, Main, and Upper. The prospect was drilled in 2017, targeting seismic amplitude anomalies that represented a combination of reservoir facies and hydrocarbons. As the first well (6608/10-17S) proved hydrocarbons down to base reservoir in Cape Vulture Main and Upper, an appraisal well with two sidetracks were planned and drilled to determine the reservoir development, pressure communication and oil-water contact. A good understanding of the lateral variation within the reservoir was of importance to the technical economical evaluation of the discovery. The appraisal wells planned for a comprehensive coring and logging program. The main objectives were to reduce the uncertainty of estimated in place volumes by establishing the depth of the hydrocarbon-water contact, prove lateral pressure communication within each reservoir level, reduce the uncertainty of lateral and vertical reservoir distribution and quality, reduce the uncertainty of hydrocarbon saturation and understand the relationship between seismic amplitude anomalies and subsurface properties / fluids. The logging program included triaxial resistivity, nuclear spectroscopy, electrical images, nuclear magnetic resonance (NMR) complementing triple combo, followed by formation pressure measurements, and fluid sampling. The presence of clay minerals in varying amounts within the reservoirs depresses the resistivity measurement and leads to underestimation of the hydrocarbon saturation when using conventional Archie’s equation - a common petrophysical challenge in such conditions. The hydrocarbon saturation is an important parameter when calculating reserves and estimating whether a discovery is of commercial value. Hence, reducing the uncertainty span on hydrocarbon saturation (total and effective) and estimating the net pay thickness is critical. Using core data and advanced down-hole measurements to optimize a resistivity-based saturation model can reduce the uncertainty of the saturation estimates. Here we document the petrophysical evaluation of the data acquired, assessing heterolithic low resistivity pay with wireline log measurements combined with core data. Focus on the coring strategy, recommendations on sampling intervals for the core analysis, and key logging measurement requirements. The results show substantial improvements in the understanding of the hydrocarbon saturation, ultimately increasing in-place volume estimates. The integrated analysis, including NMR measurements, helps to delineate the fluid contacts, further reducing the uncertainty on the recoverable net pay thickness. The core data validate the independent log-based laminated sand analysis. This illustrates how an integrated approach combining core measurements, logs, and formation testing provide an accurate evaluation of low resistivity pay reservoirs, reducing the uncertainty in the technical economical evaluation.