diff --git a/Tests/Notebooks/SimplePETSc.ipynb b/Tests/Notebooks/SimplePETSc.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5e62dbdc40705fdbca46b28188dcee70a61865d2
--- /dev/null
+++ b/Tests/Notebooks/SimplePETSc.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "bb0907b4-4e26-4c4e-ab1f-22b5330cb1d2",
+   "metadata": {},
+   "source": [
+    "title = \"SimplePETSc\"\n",
+    "date = \"2021-11-09\"\n",
+    "author = \"Lars Bilke\"\n",
+    "notebook = \"SimplePETSc.ipynb\"\n",
+    "[menu]\n",
+    "  [menu.benchmarks]\n",
+    "    parent = \"elliptic\"\n",
+    "<!--eofm-->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3ca5c4d-596c-4820-b812-3499044db25e",
+   "metadata": {},
+   "source": [
+    "The following shows running a simple steady-state diffusion benchmark running on 2 cores."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7962f42f-fd53-4fc1-b966-a8ba924aca6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "prj_name = \"square_1e1_neumann\"\n",
+    "prj_file = f\"{os.getcwd()}/../Data/EllipticPETSc/{prj_name}.prj\"\n",
+    "out_dir = f\"_out/{prj_name}\"\n",
+    "\n",
+    "if not os.path.exists(out_dir):\n",
+    "    os.makedirs(out_dir)\n",
+    "\n",
+    "! mpirun -np 2 ogs -o {out_dir} {prj_file} > out.log"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e2e294c-e803-4f02-b5ab-9bdfef94b00f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'p')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import vtuIO\n",
+    "\n",
+    "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{prj_name}.pvd\", dim=2)\n",
+    "time = pvdfile.timesteps\n",
+    "points={'pt0': (0.3,0.5,0.0), 'pt1': (0.24,0.21,0.0)}\n",
+    "pressure_linear = pvdfile.read_time_series(\"pressure\", points)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(time, pressure_linear[\"pt0\"], \"b-\", label=\"pt0 linear interpolated\")\n",
+    "plt.plot(time, pressure_linear[\"pt1\"], \"r-\", label=\"pt1 linear interpolated\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34dfbd92-d5fa-49c0-9e04-6a4b04fd2ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/ci/jobs/jupyter.yml b/scripts/ci/jobs/jupyter.yml
index 4d080aa9d6c3ca4904394754c79fa5a77f8eeb1e..22e3e783c7fdcd5af9c7751cfb1f3928b4d447e8 100644
--- a/scripts/ci/jobs/jupyter.yml
+++ b/scripts/ci/jobs/jupyter.yml
@@ -37,14 +37,14 @@ build jupyter:
       --tag $DOCKER_TAG_JUPYTER_PETSC
       $ON_MASTER_ARGS
     - cd $CI_PROJECT_DIR
-    # execute notebooks in container
+    # execute notebooks in (PETSc) container
     - >
       docker run --rm -v $PWD:/home/jovyan/work --user `id -u $USER` --group-add users
-      --workdir /home/jovyan/work $DOCKER_TAG_JUPYTER
+      --workdir /home/jovyan/work $DOCKER_TAG_JUPYTER_PETSC
       bash -c "find Tests/Notebooks -type f -iname '*.ipynb' -not -path \"*.ipynb_checkpoints*\" | xargs -n1 jupyter nbconvert --execute --inplace"
     - >
       docker run --rm -v $PWD:/home/jovyan/work --user `id -u $USER` --group-add users
-      --workdir /home/jovyan/work $DOCKER_TAG_JUPYTER
+      --workdir /home/jovyan/work $DOCKER_TAG_JUPYTER_PETSC
       bash -c "find Tests/Notebooks -type f -iname '*.ipynb' -not -path \"*.ipynb_checkpoints*\" | xargs -n1 nb2hugo --site-dir web --section docs/benchmarks/notebooks --template Tests/Notebooks/nbconvert_templates/collapsed.md.j2"
   artifacts:
     name: container