{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SQIGDCPJCZZNTEQ6RW2VC25M27","short_pith_number":"pith:SQIGDCPJ","schema_version":"1.0","canonical_sha256":"94106189e91672d9921e8db5516bacd7d0a282a8fee6a03553759e810d7a7e06","source":{"kind":"arxiv","id":"1901.02549","version":2},"attestation_state":"computed","paper":{"title":"Deep Neural Networks Predicting Oil Movement in a Development Unit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Sitnikov, Alexey Akhmetov, Andrey Margarit, Dmitry Koroteev, Evgeny Burnaev, Ivan Oseledets, Maxim Simonov, Pavel Temirchev, Ruslan Kostoev","submitted_at":"2019-01-08T23:08:27Z","abstract_excerpt":"We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it \"Metamodel\") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accur"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.02549","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-08T23:08:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e16970d8b51bb670a779e90ff3ca09e8ff3dbafb92d9a937d06899d3049a7ff2","abstract_canon_sha256":"cfcce8ade3766d6b35b12b141259a32a1f83365595db66667e2143591af8980d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:23.360162Z","signature_b64":"su/xu65D2VlpRDm3cG5W1RFK+rbtSs6PYeHRBJ+21XaIabYp7rt0c49Vge8e0rTciiV97hJToNRhjX1fZ/joAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94106189e91672d9921e8db5516bacd7d0a282a8fee6a03553759e810d7a7e06","last_reissued_at":"2026-05-17T23:43:23.359529Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:23.359529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Neural Networks Predicting Oil Movement in a Development Unit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Sitnikov, Alexey Akhmetov, Andrey Margarit, Dmitry Koroteev, Evgeny Burnaev, Ivan Oseledets, Maxim Simonov, Pavel Temirchev, Ruslan Kostoev","submitted_at":"2019-01-08T23:08:27Z","abstract_excerpt":"We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it \"Metamodel\") is based on a projection of the system dynamics into a latent variable space, using Variational Autoencoder model, where Recurrent Neural Network predicts the dynamics. We show that being trained on multiple results of the conventional reservoir modelling, the Metamodel does not compromise the accur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02549","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.02549","created_at":"2026-05-17T23:43:23.359620+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.02549v2","created_at":"2026-05-17T23:43:23.359620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02549","created_at":"2026-05-17T23:43:23.359620+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQIGDCPJCZZN","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQIGDCPJCZZNTEQ6","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQIGDCPJ","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27","json":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27.json","graph_json":"https://pith.science/api/pith-number/SQIGDCPJCZZNTEQ6RW2VC25M27/graph.json","events_json":"https://pith.science/api/pith-number/SQIGDCPJCZZNTEQ6RW2VC25M27/events.json","paper":"https://pith.science/paper/SQIGDCPJ"},"agent_actions":{"view_html":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27","download_json":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27.json","view_paper":"https://pith.science/paper/SQIGDCPJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.02549&json=true","fetch_graph":"https://pith.science/api/pith-number/SQIGDCPJCZZNTEQ6RW2VC25M27/graph.json","fetch_events":"https://pith.science/api/pith-number/SQIGDCPJCZZNTEQ6RW2VC25M27/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27/action/storage_attestation","attest_author":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27/action/author_attestation","sign_citation":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27/action/citation_signature","submit_replication":"https://pith.science/pith/SQIGDCPJCZZNTEQ6RW2VC25M27/action/replication_record"}},"created_at":"2026-05-17T23:43:23.359620+00:00","updated_at":"2026-05-17T23:43:23.359620+00:00"}