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32Enterprise

K&R Operating LLC

Saas-based solution for energy sector

ReactPythonPostgreSQLSCADA APIsDockerTimescaleDB
K&R Operating LLC — Saas-based solution for energy sector
+Overview

Project overview

K&R Operating LLC is Saas-based solution for energy sector. K&R Operating LLC offers services in natural gas compression, power generation, and process equipment for the energy sector across the U.S. Built with high standards for performance, security, and intuitive user experiences, the platform is engineered to support mission-critical workflows and seamless scale.

+Industries
EnterpriseEnterpriseDigital InnovationScalable Architecture
+Tech Stack
ReactPythonPostgreSQLSCADA APIsDockerTimescaleDB
// 01The Challenge

What was the problem or pain point

Client Dilemma & Technical Bottleneck

K&R Operating LLC manages oil and natural gas production wells, saltwater disposal facilities, and pipeline gathering systems in the Permian Basin. Field pumpers and petroleum engineers relied on manual physical well site inspections, paper gauge sheets, and isolated SCADA systems. When wellhead pressures spiked or rod pumps suffered mechanical failures overnight, wells would shut down or overflow undetected for hours, resulting in thousands of barrels of lost production, environmental spill hazards, and costly emergency repairs.

// 02Strategic Advice

What we suggested to the client

Solid Sphere Architectural Recommendation

Solid Sphere proposed developing an intelligent, real-time oilfield SCADA telemetry and predictive maintenance platform. We recommended deploying an industrial IoT ingestion gateway that streams pressure, temperature, flow rate, and pump vibration metrics into a time-series database, paired with automated anomaly detection algorithms that alert field engineers to impending equipment failures hours before physical shutdowns occur.

+Key Complexities

What made this project challenging

01

Ingesting live telemetry data from distributed oil and gas wellheads across remote rural basins

02

Detecting pump malfunctions, pressure drops, and tank overflow anomalies in real time

03

Replacing manual paper gauge run-tickets with digital field technician verification workflows

04

Managing automated regulatory compliance reporting for environmental protection agencies

// 03Engineering Execution

What was implemented and how it was implemented

Step-by-Step Technical Execution

We developed a centralized oilfield monitoring platform using React on the frontend and Python with TimescaleDB on the backend. We built custom SCADA protocol adapters (Modbus, MQTT, OPC-UA) that securely ingest telemetry from electronic flow meters and rod pump controllers deployed across hundreds of well sites. The data streams into TimescaleDB, where our Python analytics workers calculate dynagraph card patterns, rod pump fillage percentages, and casing pressure trends. We created an interactive GIS map and well telemetry dashboard in React, displaying color-coded well statuses, production decline curves, and instant alarm notifications. When automated anomaly detection detects abnormal torque or pressure spikes, the system automatically dispatches priority SMS and voice alerts to field pumpers' mobile devices and can trigger automated safety valve shutdowns.

+Key Deliverables

How we delivered

1

Engineered IoT telemetry ingestion pipeline utilizing TimescaleDB for high-velocity sensor data

2

Built real-time well status web dashboard with interactive tank level indicators and pressure gauges

3

Developed offline-capable mobile app for field pumpers to log daily meter readings and inspections

4

Implemented automated anomaly detection alerting field operators instantly via SMS and push notifications

// 04Architecture & Stack

What did we use to implement it

Technology Stack & Technical Rationale

React delivered a responsive, high-density industrial telemetry dashboard with interactive time-series charting and GIS satellite overlays. Python with FastAPI handled SCADA protocol ingestion and real-time anomaly calculation. TimescaleDB (PostgreSQL time-series engine) provided scalable, high-throughput storage for millions of sensor data points daily. Docker containerization ensured consistent deployment across cloud servers and on-premise oilfield edge gateways.

ReactPythonPostgreSQLSCADA APIsDockerTimescaleDB
+Results

Key outcomes

01

Reduced well downtime and production loss by 35%

02

Eliminated 100% of paper run-ticket processing delays

03

Monitored over 500 active oil and gas wells 24/7

04

Full compliance with state and federal environmental reporting

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