PetroFlow

PetroFlow is a library that allows to process well data (logs, core photo etc.) and conveniently train machine learning models.

Main features:

• load and process well data:
  • well logs
  • core images in daylight (DL) and ultraviolet light (UV)
  • core logs
  • inclination
  • stratum layers
  • boring intervals
  • properties of core plugs
  • lithological description of core samples
• perform core-to-log matching
• predict porosity by logs
• detect mismatched DL and UV core images
• recover missed logs and DT log by other logs
• detect collectors by logs

About PetroFlow

PetroFlow is based on BatchFlow. You might benefit from reading its documentation. However, it is not required, especially at the beginning.

PetroFlow has two main modules: src and models.

src module contains Well, WellBatch, CoreBatch and WellLogsBatch classes. Well is a class, representing a well and includes methods for well data processing. All these methods are inherited by WellBatch class and might be used to build multi-staged workflows that can also involve machine learning models. CoreBatch is a class for core images processing, especially for detection of mismatched pairs. WellLogsBatch allows working with well logs separately.

models module provides several ready to use models for important problems:

• logs and core data matching
• predicting of some reservoir properties (e.g., porosity) by well logs
• detecting mismatched pairs of DL and UV core photos
• logs recovering (e.g., DT log) by other logs
• detecting of oil collectors by logs

Basic usage

Here is an example of a pipeline that loads well data, makes preprocessing and trains a model for porosity prediction for 3000 epochs:

train_pipeline = (
  bf.Pipeline()
    .add_namespace(np)
    .init_variable("loss", init_on_each_run=list)
    .init_model("dynamic", UNet, "UNet", model_config)
    .keep_logs(LOG_MNEMONICS + ["DEPTH"])
    .interpolate(attrs="core_properties", limit=10, limit_area="inside")
    .norm_min_max(q1, q99)
    .random_crop(CROP_LENGTH_M, N_CROPS)
    .update(B("logs"), WS("logs").ravel())
    .stack(B("logs"), save_to=B("logs"))
    .swapaxes(B("logs"), 1, 2, save_to=B("logs"))
    .array(B("logs"), dtype=np.float32, save_to=B("logs"))
    .update(B("mask"), WS("core_properties")["POROSITY"].ravel())
    .stack(B("mask"), save_to=B("mask"))
    .expand_dims(B("mask"), 1, save_to=B("mask"))
    .divide(B("mask"), 100, save_to=B("mask"))
    .array(B("mask"), dtype=np.float32, save_to=B("mask"))
    .train_model("UNet", B("logs"), B("mask"), fetches="loss", save_to=V("loss", mode="a"))
    .run(batch_size=4, n_epochs=3000, shuffle=True, drop_last=True, bar=True, lazy=True)
)

Installation

PetroFlow module is in the beta stage. Your suggestions and improvements are very welcome.

PetroFlow supports python 3.5 or higher.

Installation as a python package

With pipenv:

pipenv install git+https://github.com/gazprom-neft/petroflow.git#egg=petroflow

With pip:

pip3 install git+https://github.com/gazprom-neft/petroflow.git

After that just import petroflow:

import petroflow

Installation as a project repository

When cloning repo from GitHub use flag --recursive to make sure that batchflow submodule is also cloned.

git clone --recursive https://github.com/gazprom-neft/petroflow.git

Citing PetroFlow

Please cite PetroFlow in your publications if it helps your research.

Khudorozhkov R., Kuvaev A., Kozhevin A. PetroFlow library for data science research of well data. 2019.

@misc{
  author       = {R. Khudorozhkov and A. Kuvaev and A. Kozhevin},
  title        = {PetroFlow library for data science research of well data},
  year         = 2019
}