Saudi Arabian Oil Company

Nazih F Najjar

Matter J Alshammery

Aus A Tawil

Mohammad O Amoudi

Mokhles Mustapha Mezghani

Sedat Inan

Rainer Zuhlke

Mahdi Abuali

Conrad K Allen

Systems and methods include a method for predicting geological formation tops. First well log data associated with a key master well is received. Formation data identifying tops of formations confirmed in the key master well is received. Merged key master well and formation data is generated in a dynamic time warping (DTW)-readable format by merging the first well log data with the formation data. Second well log data associated with a training well located in geographic proximity to the key master well is received. The second well log data is formatted into the DTW-readable format. A DTW function is executed to generate indices associated with the formation tops. The DTW function uses the merged key master well and formation data and the formatted second well log data as DTW function inputs. Predicted geological formation tops for the training well are predicted using the generated indexes.

Automatic geological formations tops picking using dynamic time warping (DTW)

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

Optimization of well-planning process for identifying hydrocarbon reserves using an integrated multi-dimensional geological model

Identifying hydrocarbon reserves of a subterranean region using a reservoir earth model that models characteristics of the region

Allocating resources for implementing a well-planning process

A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.

Iterative and repeatable workflow for comprehensive data and processes integration for petroleum exploration and production assessments

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Dhahran, Saudi Arabia

Nazih F Najjar