Innovation in Oil and Gas Exploration and Production in Saudi Arabia: Enhancing Efficiency and Expanding Upstream Capacity

An upstream oil & gas company in Saudi Arabia focusing on exploration of new fields and optimizing production from existing oilfields.

Many of the client’s oil fields were mature, and finding new reserves was difficult. Traditional exploration methods were slow and could miss complex reservoirs. The company wasn’t leveraging modern techniques like AI-driven seismic analysis and digital reservoir modeling. Additionally, recovery was suboptimal as advanced Enhanced Oil Recovery (EOR) methods were not widely used. The client needed to deploy cutting-edge exploration and production techniques to sustain output.

We integrated exploration and production optimization program**. On the exploration front, we introduced advanced geoscience techniques: employing 3D seismic surveys with high-resolution processing and applying AI-driven algorithms to seismic and geological data to better identify oil-bearing structures (especially in geologically complex areas). The solution also included a new prospect prioritization model that uses machine learning to evaluate geological prospects based on analogs and success data, thereby guiding geologists to the most promising leads. For production, the solution focused on implementing Enhanced Oil Recovery (EOR) methods and smart field technologies. We designed a plan to roll out EOR pilots – for instance, CO₂ injection in a mature field with declining pressure, and chemical polymer flooding in another field – with the goal of boosting recovery rates if successful. Alongside EOR, we recommended deploying digital oilfield technologies: instrumenting wells with sensors (pressure, temperature, flow) and linking them to a real-time production monitoring system for each field. This would allow reservoir engineers to perform continuous reservoir simulation updates (essentially digital twin models of the reservoirs) and optimize well settings or injection rates on the fly. The solution called for drilling optimization as well – using AI tools to guide drill-bit navigation and geosteering to stay in sweet spots of the reservoir. Finally, we integrated a knowledge transfer component, partnering the client’s international experts in EOR and digital fields to build internal capabilities.

We tackled the problem in phases, starting with data and diagnostics. Our exploration consultants reviewed the client’s existing seismic and well dantegrated regional geological studies and satellite data. We then brought in a data science team to develop and train the AI models – for example, feeding historical data of known discoveries vs. dry wells into a model to discern subtle seismic signatures that correlate with successful finds. In parallel, our reservoir engineers assessed the current recovery factors in the client’s main fields and identified which fields had the most potential for EOR (considering factors like oil viscosity, reservoir deity of injection gas, etc.). We then ran simulation studies to estimate how much additional oil each EOR method could yield and at what cost, helping to prioritize the EOR projects. For the digital enablement, we conducted a pilot in one medium-sized oilfield: installing downhole sensors and surface data transmission equipment on a handful of wells, and deploying a software platform for real-time monitoring and control. Over a few months, we observed how this pilot improved reaction time to changes (e.g., quickly adjusting pump rates when water cut increased). We used lessons from the pilot to refine the implementation plan for wider rollout. Throughout the project, close collaboration with the client’s geoscientists and engineers was maintained – we held joint interpretation sessions, field visits, and scenario planning workshops. This not only ensured our recommendations were practical but also helped train the client’s staff in the new tools and methods.

Our recommendations were comprehensive. For exploration: we recommended the client invest in reprocessing existing seismic data with the latest algorithms to glean new insights from old data, and acquire new seismic surveys (including 3D and even 4D time-lapse seismic) in underexplored areas. We advised integrating AI-based prospect analysis into the exploration workflow to augment the geoscientists’ interpretations and flag prospects that might have been overlooked by human analysis. Furthermore, we suggested a focused exploration campaign for unconventional resources (like shale gas/oil), including hiring or contractinin that domain. For production: we recommended scaling up Enhanced Oil Recovery methods on fields where simulations showed substantial gains – for example, implement a full-scale CO₂ injection project in Field A which could raise recovery by say 10–15%, and a water-polymer flood in Field B. We also advocated for rolling out the “smart field” infrastructure across all major fields over the next few years – equipping wells with sensors and automation, and establishing a central operations center where engineers can monitor multiple fields in real time. Thaining personnel to manage this high-tech setup. Additionally, we recommended optimizing the well portfolio: for instance, performing workovers on certain wells to install intelligent completions (valves that can be remotely controlled) and selectively drilling new infill wells in areas indicated by updated reservoir models. A periodic reservoir management review process was suggested, where every field is reviewed annually with fresh data and simulations to decide on actions (like adjusting injection patterns or drilling a new well). Finally, we emphasized knowledge management – capturing learnings from each pilot and project so that successes can be replicated across the organization.

The client started seeing positive results in both exploration and production domains. With the help of AI and advanced seismic re-analysis, the exploration team discovered a new viable oil structure in a region that was previously p find that added significantly to the company’s reserves, something that hadn’t happened in years. The use of data-driven prospecting improved the overall success rate of exploration wells, saving money on unnecessary drilling. On the production side, initial EOR pilots showed strong results: the CO₂ injection in one mature field measurably increased oil output and slowed the decline rate, validating the decision to scale up EOR. Once the digital oilfield technologies were implemented more broadly, the company experienced a reduction in production downtime and a small but meaningful uptick in overall production efficiency. For example, by monitoring wells continuously and optimizing pump settings, some fields saw a few percent increase in daily output without any new drilling – essentially “free” barrels that were previously lost to suboptimal operation. Importantly, the combination of better data and smart automation led to an increased recovery factor outlook for several fields, extending their productive life and future cash flows. The company’s unit cost of production improved as well, since they were extracting more oil with the same or lower effort. Moreover, by adopting these advanced practices, the client positioned itself among the technologically advanced operators, which will pay dividends in the long run as the industry moves toward more digital and efficient operations. The ROI from this engagement is not only measured in immediate barrels and cost savings, but also in the strengthened capability of the client’s team, who are now proficient in cutting-edge exploration and production techniques.