resistivity

Oil production from shale and tight formations will increase to more than 6 million barrels per day (b/d) in the coming decade, making up most of total U.S. oil production (> 50%). However, achieving an accurate formation evaluation of shale faces many complex challenges. One of the complexities is the accurate estimation of shale properties from well logs, which is initially designed for conventional reservoirs. When we use the well logs to obtain shale properties, they often cause some deviations. Therefore, in this work, we combine cores and well logs together to provide a more accurate guideline for estimation of total organic carbon, which is primarily of interest to petroleum geochemists and geologists.

Our work is based on Archie's equation. Resistivity log will lead to some incorrect results, such as total resistivity, when we follow the conventional interpretation procedure in well logs. Porosity is another complex parameter, which cannot be determined only by well log, i.e. density, NMR, and Neutron log. Therefore, the flowchart of TOC calculation includes five main parts: (I) the shale content calculation using Gamma log; (II) the determination of shale distributions using Density and Neutron logs and cross-plot; (III) the calculation of total resistivity at different distribution types; (IV) obtaining porosity using core analysis, NMR and density logs; and (V) the calculation of TOC from modified Archie's equation.

The results indicate that the shale content has a strong effect on estimation of water saturation and hydrocarbon saturation. Especially, the effect of shale content is exacerbated at a low water saturation. A more accurate flowchart for TOC calculation is established. Based on Archie's equation, we modify total resistivity and porosity by combining Gamma Log, Density Log, Neutron Log, NMR Log, and Cross-plot. An easier way to estimate porosity is provided. We combine the matrix density and kerogen density together and obtain them from core analysis. Poupon's et al. (1954) laminar model has some limitations when applying in shale reservoirs, especially at a low porosity.

Literature surveys show few studies on the flowchart of TOC calculation in shale reservoirs. This paper provides some insights into challenges of well logs, core analysis in shale reservoirs and a more accurate guideline of TOC calculation in shale reservoirs.

Characterizing the fractures is an important task to improve the understanding and utilization of hydraulic fracturing. As an approach to augment and improve on the existing methods, time-lapse electric potential measurements could be used to characterize subsurface features. In this study we investigated the characterization of fracture length and fracture density by using time-lapse electric potential data. A new borehole ERT (electric resistivity tomography) method designed specifically for hydraulic fracture characterization is proposed to better capture reservoir dynamics during hydraulic fracturing. This method uses high resolution electric potential data by implementing electrodes in or near boreholes and monitor electric potential distribution near the horizontal fracture zone. The time-lapse electric potential data generated by this tool were simulated and subsequently used to analyze fracture characteristics. Inverse analysis was then performed on the electric potential data to estimate fracture length and fracture density. Last, we performed sensitivity analysis to examine the robustness of the estimates in nonideal environments. The results of this work show that time-lapse electric potential data are capable of capturing flow dynamics during the fracturing process. Using the proposed borehole ERT method we successfully estimated the true fracture length and true fracture density of a constructed fracture model. We were able to determine the best locations in the constructed reservoir to place the electrodes, and through sensitivity analysis we found the maximum noise level of the electric potential data that can still allow the proposed method to make robust fracture length and fracture density estimates.

Our proposed method offers a new approach to make robust estimates of fracture length and fracture density. Electric potential data have been used mostly for well logging in the past. This study demonstrates a novel way of using electric potential data in unconventional development and opens possibilities for more applications such as production monitoring.

The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical.

More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis.

These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.

Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. In this study, functional network tool is used to develop a model to predict water saturation using petrophysical well logs as input data and the dean-stark measured water saturation as an output parameter. The data comprised of more than 200 well log points corresponding to available core data. The developed FN model was optimized by using several optimization algorithms such as differential evolution (DE), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES). FN model optimized with PSO found to be the most robust artificial intelligence (AI) model to predict water saturation in carbonate rocks. The results showed that the proposed model predicted the water saturation with an accuracy of 97% when related to the experimental core values. In this study in addition to the development of optimized FN model, an explicit empirical correlation is also extracted from the optimized FN model. To validate the proposed correlation, three most commonly applied water saturation models (Simandoux, Bardon and Pied model, Fertl and Hammack Model, Waxman-Smits, and Indonesian) from literature were selected and subjected to same well log data as the AI model to estimate water saturation. The estimated water saturation values for AI and other saturation models were then compared with experimental values of testing data and the results showed that AI model was able to predict water saturation with an error of less than 5% while the saturation models did the same with lesser accuracy of error up to 50%. This work clearly shows that computer-based machine learning techniques can determine water saturation with a high precision and the developed correlation works extremely well in prediction mode.

This paper presents a novel borehole image compression algorithm for real-time (RT) logging while drilling (LWD). The compression scheme is designed to optimize the critical information required for RT decision making at low telemetry bandwidths. In the proposed algorithm we estimate the structure of the image (i.e. the amplitude and phase shift of the dip) and modify the encoding dictionary based on the features. The resulting dictionary resembles sinusoidal features, thus optimizing the reconstruction of bedding or other planar features in deviated wells. The dictionary is designed using a modified version of the 2D discrete wavelet transform (DWT). This approach has a low encoding complexity and supports the integration of directional information into the transform. Since feature estimation is a challenging step, we use a classifier to identify when directional information should be added to the transform or whether a conventional implementation is used. The algorithm has been implemented in both oil-and water-based mud LWD imager tools, where the low encoding complexity has facilitated the implementation in legacy tools with limited computation resources. We present field test results comparing the borehole images from RT and recorded mode (RM) data from one of the industry's first RT LWD resistivity images obtained from a well drilled using oil-based mud.

Openhole logging tools have been used without wireline in memory logging for 20 years, in an important and growing market. A new system in field trials in Canada and Russia in 2019 further expands the operating envelope overlap between wireline and logging-while-drilling by making step changes in communications, autonomy, performance, and reliability. The new approach advances the logging of horizontal and challenging wells, and permits operations in managed pressure drilling and foam drilled wells.

The vast majority of openhole memory work is achieved with a hydro-mechanical system that indicates successful deployment but lacks two-way communication between the engineer at surface and the tools downhole. Pressure-pulse communications have been used for 10 years with a wide range of measurements including memory logging with wireline formation testers. The experience gained from operating these systems informed the development of a new system that uses drillpipe rotation to communicate to the tools, pressure pulses to reply for the uplink, and a more powerful downhole processor. These enhancements in autonomy and communication improve the outcome of logging jobs.

The system incorporates a new rotation downlink method which employs data from an angular rate sensor to identify a series of commands sent by rotating the drillstring. Control software in the downhole tools executes the commands, and replies are transmitted uphole by pressure pulses. The toolstring is released from a safe ‘garage’ position inside the drillpipe and deployed into openhole, with the top of the toolstring retained by a no-go. The engineer is supplied with far more diagnostic information than previously, including the axial position of the tools, with context sensitive encoding to provide maximum troubleshooting information to the surface over a limited bandwidth channel. The pressure-pulse downlink remains in place as a secondary method. Other material improvements include high data sampling rate, debris tolerance and downhole recovery strategies. All of these advances improve the autonomy of the downhole memory equipment as well as the real-time communication and control from the surface.

Autonomous memory logging toolstrings, with powerful downhole software and rotation downlink communications, are important components in improving the performance and reliability of these successful and innovative formation evaluation systems.

Low electrical resistivity and low contrast reservoirs (LRLC) pay zones are composed of thinly-bedded laminated layers containing hydrocarbon accumulations surrounded by non-reservoir layers indicating lack of resistivity contrast. These pay zones are difficult to be distinguished at seismic and log scale due to lower vertical and lateral resolution. Traditionally, deep-resistivity logs in LRLC zones read 0.5 to 5 ohm-m. Low contrast pay zone occurs mainly when the formation waters are fresh or having low salinity resulting in a very little resistivity contrast between oil and water zones. Major challenges imposed in LRLC reservoirs include identification, characterization, and evaluation of the hydrocarbon interval, which is usually masked by the lack of resistivity contrast between the hydrocarbon and water zones. The identification and characterization of the lowdown on resistivity pay is essential for the re-development of mature assets for improved oil recovery. This paper deals with the characterization of low resistivity hydrocarbon-bearing thinly-bedded reservoirs from a brownfield.

To unlock the hidden potential of LRLC pay sands in the offshore Sarawak Malaysia, the effective integration of subsurface disciplines including petrophysics, geology and quantitative derivatives from the seismic analysis is vital. This study covers the geological perspective of low contrast reservoirs from an offshore oil field deposited in lower coastal plain settings located within offshore Sarawak Malaysia. An improved understanding of the geological, petrophysical and geophysical parameters was achieved by adopting a holistic and multidisciplinary approach. This includes the integration of core, logs, rock physics modeled parameters, stratigraphic, depositional and lithofacies information along with stochastic inversion derivatives. Acoustic Impedance shows the facies changes in broader terms between producing and non-producing zone.

The paper quantifies rock physics parameter uncertainties for LRLC pay zones and establishes a framework for LRLC reservoir characterization. Stochastic inversion derived P-Impedance and Vp/Vs ratio are used to predict fluid and facies probabilities (Rajput S., 2014) for LRLC reservoirs, which then further integrated with stratigraphic information. The results offered an effective way of establishing analogs of producing and non-producing LRLC zones. Analysis of fluid and facies probabilities derivatives driven surface attributes is a way seismic can potentially contribute to indicating areas of relatively better or worse LRLC reservoir continuity.

Identified LRLC reservoirs proved to be of commercial-quality and increased oil production to the extent of several hundred thousands of barrels over the years and currently producing. Rock physics modeled parameters including AI and Vp/Vs are sensitive to LRLC pay zones and their effective integration with image logs, lithofacies, and seismic inversion lead to reduce uncertainties in infill drilling programs. Geological understanding of the possibility of LRLC occurrences is required to assess oil and gas bypassed oil. Detailed geological features are clearly resolved in high-definition image logs. Low resistivity pay zones present in the main reservoir intervals can be identified by integrating the information from low gamma ray, low impedance, and low resistivity zones collectively. The results of this study show the value of integrated approaches and improvements in reservoir description from stochastic inversion into reservoir models.

Accurate predictions of connectivity and heterogeneity pose important technical challenges for successful maturation of conventional and unconventional reservoirs. We present the success of a new reservoir management workflow that uses both artificial intelligence and classic models to define the impact of stratigraphic connectivity and heterogeneity on horizontal-well production performance in a mature heavy oil field. The data-driven model based on fuzzy logic was used to compute a new attribute named dynamic Reservoir Quality Index (dRQI). The classical models used the stratigraphic Lorenz Plots, Reservoir Quality Index (RQI) and Flow-Zone indicator (FZI). Workflows were validated through a lookback process on more than 400 wells used to predict the fine-scale stratigraphic and directional heterogeneities within intervals targeted by horizontal wells, and production performance. The workflow was successfully used to optimize the horizontal well placement for 2019-2020 drilling programs.

Ultradeep resistivity logging-while-drilling (LWD) is now a routine service for real-time well landing,geosteering, and reservoir and fluid contact evaluation. Progressing beyond layered earth inversions to three-dimensional (3D) inversions helps improve real-time decisions to deliver better well placement, completion, and production. To this end, the first real-time 3D inversion of ultradeep resistivity LWD data is realized by exploiting the fact that the sensitive volume of a given transmitter-receiver pair is far smaller than the total logging volume. This implies that the global mesh can be decoupled into multiple independent, localized inversion and modeling meshes that are tractable for the efficient solution of the forward and inverse problems in real time using moderate computer resources. The authors' implementation is based on a 3D finite-volume method discretized on locally refined octree meshes. It uses the regularized Gauss-Newton method for minimizing the objective function for data subsets on local inversion meshes, which iteratively update the global mesh. Nonlinear Kalman filtering is applied using prior information on each local inversion mesh from the updated global mesh to introduce new observations optimally. A model study and a case study of trilateral well placement in a mature reservoir in the Norwegian Continental Shelf demonstrate the efficacy of the method. Run times on modest computer resources enable the first real-time 3D inversion of ultradeep resistivity LWD data.

Eastern Siberia is characterized by the extremely complex geological structure. The main factors include multiple faults, trappean and salt tectonics, the complex structure of the upper part of the section (0–1200 m) and its high-velocity characteristic (5000–6000 m/s), the high degree of rock transformation by secondary processes, low formation temperatures (10–30°C), the mixed fluid composition (gas, oil and water), and low net thicknesses (5–7 m) of productive layers. The fields of the region are among the most complex ones in the world according to the BP Company's statistics. New seismic and geologic model based on complex analyses of core, well logs, well tests, seismic and electromagnetic data allowed the Gazpromneft-GEO company to drill a series of successful wells.

Gazpromneft-GEO, LLC.holds three oil and gas exploration and production licenses within the Ignyalinsky, Vakunaisky and Tympuchikansky (Chona field) subsurface blocks (Russia, Eastern Siberia, Irkutsk Region and Republic of Sakha (Yakutia)). The area of the blocks is 6,855 sq.km, 3,050 sq.km of which are covered by the 3D seismic and high-density electric prospecting (Fig. 1). 70 exploration wells were drilled. The geological oil reserves of the Chona field are about 500 Mt. Nonetheless, the level of study of the blocks is extremely irregular, despite such significant reserves. To transfer from the exploration stage to production the capital-intensive scope of exploration is required, which expands the exploration program in time and can influence the final economic profitability of the project. The optimization of drilling costs by minimization of drilling wells and the efficiency of their drilling is the key purpose of the project.

The work was carried out within the frames of scientific research and field works at the Gazpromneft-GEO, LLC. fields in Eastern Siberia. The high-density full-azimuth ground-based seismic using the UniQ technology was performed in Russia for the first time. The electric exploration with the near-field time-domain electromagnetic method was carried out along the same lines for the first time in Russia as well. This allowed to form the high-density cube of geoelectric properties. Model based on the wells (Facies model, Petrophysics model) and field geophysical data (3D seismic survey, 3D electric exploration, gravimetric survey, magnetic survey) complexation was made. The use of the approach allows to reduce the number of wells required for exploration of fields by 40%.