Saudi Arabian Oil Company

Fatai A Anifowose

Mokhles Mustapha Mezghani

Salem Hamoud Shammari

Maimona Washie

Walid K Hussein

Saleh M Badawood

Jean-Francois Mengual

Javed Ismail

A system and method for determining a net source rock thickness is disclosed. The method includes obtaining at least one well log, a first and second plurality of core samples from a wellbore, and for each of the first plurality determining a relative abundance of inorganic materials, and for each of the second plurality a set of organic material data. The method further includes determining a validated total organic content and a measured source rock thickness based on the organic material data and relative abundance of inorganic materials and determining a data and a prediction portion of the wellbore, based on the second plurality sample locations. The method still further includes determining a cut-off value for each well log, predicting a source rock thickness from the cut-off values and the net source rock thickness from a sum of the predicted and the measured source rock thicknesses.

Automated source rock net thickness prediction system and method

A method to evaluate source rock richness is claimed. The method includes obtaining a source rock sample, collecting pyrolysis data from testing of the sample, filtering the pyrolysis data to produce a filtered pyrolysis data set, extracting a plurality of elements from their respective oxides and cross-checking the filtered pyrolysis data set with the plurality of elements. The method further includes discarding a plurality of unreliable data points from the filtered pyrolysis data set to produce a cross-checked data set, matching the cross-checked data set with wireline log data, determining a minimum value and a maximum value of the wireline log data that are within the cross-checked data set, and collecting depth thicknesses corresponding to the minimum value and the maximum value. The method also includes calculating a sum of the plurality of depth thicknesses, calculating a net source rock thickness, and calculating a corresponding average total organic carbon value.

Workflow to predict source rock richness and net thickness using integrated inorganic, pyrolysis, and wireline data

A method for determining a real-time pore pressure log of a well in a reservoir, including the steps: storing existing data logs of surface drilling parameters, logging while drilling (LWD), and mud gas of existing wells in a database, storing existing pore pressure logs of the existing wells in the database, wherein the existing pore pressure logs correspond to the existing data logs, determining a relationship between the existing data logs and the existing pore pressure logs, drilling a new well into the reservoir, determining new data logs of surface drilling parameters, LWD, and mud gas of the new well while drilling the new well, inputting the new data logs of the new well into the relationship while drilling the new well, determining a real-time pore pressure log of the new well by outputting an estimated pore pressure at a certain depth by the relationship while drilling the new well.

Method for determining pore pressures of a reservoir

A method for formation pore pressure prediction involves obtaining an input parameter set while drilling a well. The input parameter set includes surface drilling parameters, logging while drilling parameters, and advanced mud gas measurements. The method further involves generating, from the input parameter set, a reduced input parameter set, by eliminating at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure, and predicting the pore pressure by applying a machine learning model to the reduced input parameter set.

Method and system for formation pore pressure prediction with automatic parameter reduction

Systems and methods include a method for generating a real-time permeability log. Historical mud gas-permeability data is received from previously-drilled and logged wells. The historical mud gas-permeability data identifies relationships between gas measurements obtained during drilling and permeability determined after drilling. A formation hydrocarbon mobility model is trained using machine learning and the historical mud gas data. Real-time gas measurements are obtained during drilling of a new well. A real-time permeability log is generated for the new well using the formation hydrocarbon mobility model and real-time gas measurements.

Real-time estimation of formation hydrocarbon mobility from mud gas data

Systems and methods include a method for generating a real-time reservoir porosity log. Historical gas-porosity data is received from previously-drilled and logged wells. The historical gas-porosity data identifies relationships between gas measurements obtained during drilling and reservoir porosity determined after drilling. A gas-porosity model is trained using machine learning and the historical gas-porosity data. Real-time gas measurements are obtained during drilling of a new well. A real-time reservoir porosity log is generated for the new well using the gas-porosity model and real-time gas measurements.

Real-time estimation of reservoir porosity from mud gas data

Systems and methods for determining a continuous grain size log from a collection of petrographic thin section images are provided. Thin section images from core samples from one or more wells may be obtained and analyzed to estimate grain sizes. Using wireline logs from the one or more wells and the estimated grain sizes, a data structure (for example, a database) of grain sizes and wireline logs at depths may be constructed. The data structure may be used to train a machine learning model. Next, a wireline tool may be used to obtain wireline logs in a new well, and a continuous grain size log may be determined from the wireline logs of using the machine learning model. Computer-readable media for determining reservoir rock grain sizes from a collection of petrographic thin section images is also provided.

Systems and methods for generating continuous grain size logs from petrographic thin section images

Embodiments of the present disclosure aim to provide advanced log scale permeability estimation systems and methods. In an embodiment, a computer system is provided and configured to perform petrographic thin section image permeability analysis and then generate log scale permeability estimations for a well using machine learning methods and techniques. In an embodiment, the advanced log scale permeability estimation systems and methods are configured with a computer system or a micro-computer system, that can be configured with a computer circuit board comprising a processor, memory, networking capability, and software. In another embodiment the log scale permeability estimations for a well are input into a reservoir simulation model to determine hydrocarbon resources and to make portfolio predictions for a well.

Resolution preserving methodology to generate continuous log scale reservoir permeability profile from petrographic thin section images

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated analysis of petrographic thin section images. In one aspect, a method includes determining a first image of a petrographic thin section of a rock sample, and determining a feature vector for each pixel of the first image. Multiple different regions of the petrographic thin section are determined by clustering the pixels of the first image based on the feature vectors, wherein one of the regions corresponds to grains in the petrographic thin section. The method further includes determining a second image of the petrographic thin section, including combining images of the petrographic thin section acquired with plane-polarized light and cross-polarized light. Multiple grains are segmented from the second image of the petrographic thin section based on the multiple different regions from the first image, and characteristics of the segmented grains are determined.

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Al-Khobar, Saudi Arabia

Fatai A Anifowose