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With the increase in activity in deep and ultra-deep water exploration, the need for geological information is penalized by its cost. Acquiring full core data in the current economic context is difficult to justify and encourages the development of less costly alternative methodologies to compensate the lack of information when coring is not included in exploration programs. This is particularly true in oil- or synthetic-base muds (OBM) where such alternative technology is still under development as opposed to the water-base mud (WBM) environment where the technology is more mature and the interpretation workflows are better established. New state-of-the-art electric wireline technologies including large-volume rotary sidewall coring, and photorealistic OBM-adapted formation imaging combined with powerful software tools for visualization and interpretation are demonstrated to provide a viable substitute for whole conventional core for a wide range of applications, especially in clastic environments.
Abstract The Triassic reservoirs of the eastern Sahara province represent one of the main oil and gas accumulations in Algeria. This clastic succession corresponds to fluvial, estuarine and shallow marine deposits characterized by common lateral and vertical facies changes that are responsible for uncertainties in the modeling of the reservoir heterogeneities. A realistic identification of the depositional environment is critical to the delineation and prediction of the best quality reservoir facies so that optimized exploitation of the reservoir can be achieved. This paper describes a methodology that was used to generate a depositional model in the Lower to Upper Triassic reservoirs of the Rhourde El-Khrouf field based on subsurface data from six wells including well logs, borehole images, cores and the regional knowledge of the basin. Structural and sedimentary dip analyses were performed both on borehole images and oriented full-bore core photos that provided at least twice as many dips than borehole images. Facies logs were derived from the integration of core calibrated electrical borehole image analysis results with open-hole log data using neural network techniques. Depositional environments were then determined from correlations based on logs stacking patterns, facies associations and dip data. These results show that the fluvial reservoirs of the Rhourde El-Krouf field are characterized by large variations from laterally extensive bodies with good interconnectedness and high net-to-gross ratios, to multi-storey ribbon bodies with poor interconnectedness and low net-to-gross ratios. This integrated approach using high resolution image logs and full-bore core photos provided a much more robust reservoir model than would be obtained from traditional approach based solely on open-hole logs. Introduction The wells studied are located in the Rhourde El Khrouf (RKF) field of the Berkine Algerian basin (Figure-1). Oil was first discovered in this field in the early to mid 90s in the Triassic "Argilo-Gréseux inférieur" - TAGI reservoirs. These sandstones have been interpreted as fluvial deposits (Turner et al, 2001 and Sabaou et al, 2003). The field corresponds to an estimated one billion barrels of oil in place, which makes it one of the most important oil fields in Algeria. Fluvial deposits are usually difficult to map and characterized in detail because of the high frequency of lateral facies changes and difficulty in identifying individual sand packages of channel and overbank deposits with similar geological characteristics. Only an integrated approach combining data from different sources can help reduce the geological uncertainties.
ABSTRACT Deepwater reservoirs often consist of highly laminated sand shale sequences, where the formation layers are too thin to be resolved by conventional logging tools. To better estimate net sand and hydrocarbon volume in place, one usually needs to rely on high-resolution borehole image logs, which can detect extremely fine layers with thickness of several millimeters. Traditionally, explicit sand counting in thin beds has been done by applying a user-specified cutoff on a 1-D high-resolution resistivity curve extracted from electrical borehole images. The workflow generally requires meticulous image QC, multiple pre-processing steps and log calibration, and the results are often highly sensitive to the cutoff selection, especially in high-salinity environments, where resistivity in pay sand can be very close to that in shale. In oil-based mud (OBM), accuracy of the cutoff method is further limited by the presence of non-conductive mud cakes and possible tool artifacts. This paper presents a new method that estimates sand fractions directly from OBM borehole images without extracting an image resistivity curve. The processing is based on an artificial neural network, which takes a 2-D borehole image array as input, and predicts sand fractions by applying a non-linear transformation to all the elements, i.e., electrical measurements from all button electrodes. A cumulative sand count can be computed after processing the borehole image logs foot by foot along an entire well. The neural network is trained on a large dataset with example images of various degrees of laminations, labeled with sand fractions identified from core photos. Upon testing, a good match has been observed between the prediction and the target output. The results have also shown advantages against another sand counting method based on texture analysis. The described method offers new opportunities of quantifying thin sands in the absence of cores, which can be used to improve petrophysical interpretation in laminated reservoirs. With appropriate tuning, a pretrained network model could also be generalized to applications in new wells or even new fields with similar depositional environments.
Abstract Since the introduction of the first micro-electrical imaging tool in 1986, wireline resistivity images have proven to be an invaluable tool for geological and petrophysical formation evaluation in wells drilled with conductive water-base drilling mud (WBM). However, until recently, wellbore images acquired in non-conductive mud had been met with some less success due to poor borehole coverage, relatively low image resolution and electrical artefacts. In 2014, an OBM-adapted imaging tool was introduced. The new tool was designed to provide improved resolution and borehole coverage as well as geological representativeness of the images. From an operations perspective, the tool sonde and hardware were designed to increase robustness and ease of logging for field engineers, and to improve operational efficiency and reduce rig time in consideration of high spread rates for deep-water drilling rigs and the overall high costs of deepwater wells. The sonde design with two sets of pads supported by spring loaded arms allow both logging down and logging up of the tool to minimize logging time. Unlike previous imaging tools, pads are applied to the formation using spring load and not pad pressure, in order to minimize stick-slip of the tool. Pads are fully gimballed, are free to tilt, and rotate around the pad axis to enable maximum contact with the borehole wall. As for the measurement physics, a high frequency current is sent into the formation which reduces the non-conductive mud electrical impedance. Amplitude and phase of this current are measured and used in the processing to create an electrical impedivity measurement. In order to cover the full range of formation resistivities, two frequency ranges are used. After acquisition, a "composite" processing technique is used in which amplitude and phase measurements from the two frequencies are processed to generate a final impedivity image that is a function of formation resistivity and dielectric permittivity. The case study presented in this paper is an Oligocene-Miocene age deep-water turbidite deposits on the passive margin of West Africa, and comprises a complex of channels and sheet sands with localized intense faulting, and tilting due to salt tectonics and diapirism. The high-resolution image enabled highconfidence classification of geologic features. The variety of geologic features ranges from fine-scale laminations and syn-depositional micro-faults with displacement of a few centimeters to variable-scale injectite features and erosive surfaces. Also, a wide variety of formation textures that represent turbidite channel and levee facies are observed, and include coarse-grained basal conglomerates, rip-up clasts and large clay clasts, debrites, dewatering and flame structures, dish structures, internal injectite structures, pyrite nodules/streaks, and deformed facies. The high resolution image can be used for a wide range of quantitative image analyses such as net pay computation, textural attribute extraction, as well as other quantitative and semi-quantitative interpretations. Today, with more than 13 case studies in West Africa and more than 250 worldwide, the image quality from this new formation imaging technology shows a great deal of improvement over previous generations of non-conductive mud imagers. The ultrahigh-resolution images from the new imaging service enables a wide spectrum of interpretations that can be directly incorporated to enhance the reservoir model and reduce geological and petrophysical uncertainties.
Deepwater reservoirs often consist of highly laminated sand-shale sequences, where the formation layers are too thin to be resolved by conventional logging tools. To better estimate net sand and hydrocarbon volume in place, one may need to leverage the high resolutions offered by borehole image logs. Traditionally, explicit sand counting in thin beds has been done by applying a user-specified cutoff on a 1D resistivity curve extracted from electrical borehole images. These workflows require multiple preprocessing steps and log calibration, and the results are often highly sensitive to the cutoff selection, especially in high-salinity environments. This paper presents a new method that estimates sand fractions directly from electrical borehole images without extracting an image resistivity curve or applying any preselected cutoffs. The processing is based on an artificial neural network, which takes the 2D borehole image array as input, and predicts sand fractions with the measurements from all button electrodes. A cumulative sand count can be computed after processing the borehole image logs along an entire well by summing up the estimated net sands. The neural network is trained and tested on a large dataset from wells in a deepwater reservoir with various degrees of laminations, and validated with sand fractions identified from core photos. Upon testing, a good match has been observed between the prediction and the target output. The results were also compared against another sand-counting method based on texture analysis, and showed advantages of yielding unbiased estimations and a lower margin of error.